MIT News - Mathematics MIT News is dedicated to communicating to the media and the public the news and achievements of the students, faculty, staff and the greater MIT community. en Wed, 04 Mar 2020 13:00:01 -0500 Agustín Rayo wins 2020 PROSE Award MIT philosophy professor&#039;s “On the Brink of Paradox” honored as one of the best books in professional and scholarly publishing. Wed, 04 Mar 2020 13:00:01 -0500 MIT Press <p>The Association of American Publishers (AAP) has announced the winners for the 2020 PROSE Awards, which annually recognize the best in professional and scholarly publishing. Among the winners is “<a href="" target="_blank">On the Brink of Paradox: Highlights from the Intersection of Philosophy and Mathematics</a>” (MIT Press, 2019) by Agustín Rayo, author and professor of philosophy at MIT.</p> <p>The book won for the textbook/humanities category. In it, Rayo, who is also associate dean of the MIT School of Humanities, Arts, and Social Sciences, offers an introduction to awe-inspiring issues at the intersection of philosophy and mathematics and explores ideas at the brink of paradox: infinities of different sizes, time travel, probability and measure theory, computability theory, the Grandfather Paradox, Newcomb's Problem, and others. The book is based on a popular course (<a href="" target="_blank">and massive open online course</a>) taught by the author at MIT.</p> <p>The AAP unveiled 49 subject category <a href="" target="_blank">winners&nbsp;</a>for the 2020&nbsp;<a href="">PROSE Awards</a>&nbsp;honoring the best scholarly works published in 2019. The winners were selected by a panel of 19 judges from the&nbsp;<a href="">157 finalists</a>&nbsp;previously identified from the more than 630 entries in this year’s PROSE Awards competition. The subject category winners announced demonstrate exceptional scholarship and have made a significant contribution to a field of study.</p> <p>“I want to congratulate the winners of this year’s PROSE Awards and recognize the 10 MIT Press books that were named finalists,” says Amy Brand, director of the MIT Press. “'On the Brink' offers unique and compelling insights into mathematics and reflects the overall mission of the MIT Press to push the boundaries of what a university press can be. We are honored to be among the other winners for this distinguished prize.”</p> <p>Another MIT Press book, “<a href="">Decomposed: The Political Ecology of Music</a>,” by Kyle Devine, also won a PROSE Award for the music and the performing arts category.</p> MIT Press, Awards, honors and fellowships, Books and authors, Faculty, Philosophy, Mathematics, School of Humanities Arts and Social Sciences QS World University Rankings rates MIT No. 1 in 12 subjects for 2020 Institute ranks second in five subject areas. Tue, 03 Mar 2020 19:01:01 -0500 MIT News Office <p>MIT has been honored with 12 No. 1 subject rankings in the QS World University Rankings for 2020.</p> <p>The Institute received a No. 1 ranking in the following QS subject areas: Architecture/Built Environment; Chemistry; Computer Science and Information Systems; Chemical Engineering; Civil and Structural Engineering; Electrical and Electronic Engineering; Mechanical, Aeronautical and Manufacturing Engineering; Linguistics; Materials Science; Mathematics; Physics and Astronomy; and Statistics and Operational Research.</p> <p>MIT also placed second in five subject areas: Accounting and Finance; Biological Sciences; Earth and Marine Sciences; Economics and Econometrics; and Environmental Sciences.</p> <p>Quacquarelli Symonds Limited subject rankings, published annually, are designed to help prospective students find the leading schools in their field of interest. Rankings are based on research quality and accomplishments, academic reputation, and graduate employment.</p> <p>MIT has been ranked as the No. 1 university in the world by QS World University Rankings for eight straight years.</p> Afternoon light streams into MIT’s Lobby 7.Image: Jake BelcherRankings, Computer science and technology, Linguistics, Chemical engineering, Civil and environmental engineering, Mechanical engineering, Chemistry, Materials science, Mathematics, Physics, Economics, EAPS, Business and management, Accounting, Finance, DMSE, School of Engineering, School of Science, School of Architecture and Planning, Sloan School of Management, School of Humanities Arts and Social Sciences, Electrical Engineering & Computer Science (eecs), Architecture, Biology, Aeronautical and astronautical engineering MIT students dominate annual Putnam Mathematical Competition Participating MIT students make history by taking all top five spots — the first time this has happened for any school. Tue, 03 Mar 2020 11:55:02 -0500 Sandi Miller | Department of Mathematics <p>Each December, thousands of undergraduates participate in the <a href="">William Lowell Putnam Mathematical Competition</a>, the premier math contest in the United States and Canada. The 80th annual exam was held on Dec. 7, 2019, and results were announced Feb. 18. For the first time in Putnam’s history, all five of the top spots in the contest, known as Putnam Fellows, came from a single school — MIT.</p> <p>MIT students also dominated the rest of the scoreboard: nine of the next 11, eight of the next 12, and 33 of the following 80 honorable mention rankings. Among the top 192 test-takers overall, 76 were MIT students.</p> <p>The 2019 Putnam Fellows, listed in alphabetical order, are seniors Ashwin Sah and Kevin Sun, junior Yuan Yao, sophomore Shengtong Zhang, and first-year Daniel Zhu. Yao and Zhang were 2018 Putnam Fellows, and Sah was a 2017 Putnam Fellow. Among the three top scorers — Sah, Zhang, and Zhu — two earned a nearly perfect score, and one (who prefers not to be named) earned a perfect score of 120 points. This is only the fifth time in Putnam's history that a test-taker received a perfect score.</p> <p>Competitors were also ranked by participating institution. Starting in 2019, the ranking is based on the three top scorers from each institution (while in previous years, it was based on the scores of three preselected individuals). MIT came in first as a team since the three top scorers, Sah, Zhang, and Zhu, are all from MIT. This is the MIT team’s fifth first-place win in the past seven years. This year, Harvard University came in second and Stanford University came in third.</p> <p>The Department of Mathematics will also honor two top-scoring female students, first-year Dain Kim and junior Qi Qi, at an awards dinner that will be held in the spring. Qi was one of three recipients for the 2019 Elizabeth Lowell Putnam Prize, given to top female contestants. She is the fourth MIT student to receive this honor since the award began in 1992.&nbsp;</p> <p>The honors come with cash awards. The institution with the first-place team receives $25,000, and each member of the team receives $1,000. Each Putnam Fellow receives $2,500, the next 11 highest-ranking individuals each receive $1,000, and the next 12 highest-ranking individuals each receive $250. The Elizabeth Lowell Putnam Prize carries a $1,000 award, and the Department of Mathematics will also give a $1,000 special prize to Dain Kim.</p> <p>“This was unprecedented,” says <a href="">Yufei Zhao</a>, Class of 1956 Career Development Assistant Professor of Mathematics, who coaches first-year students for the competitions via the Putnam Seminar in the fall, and also oversees the competition at MIT. “It was a pretty surreal result. I am extremely proud of our students’ phenomenal performance at the Putnam Competition. We are very happy to see that our undergraduate community is home to such an exceptional group of students.”</p> <p>The Department of Mathematics’ <a href="">PRIMES</a> program, which attracts many top high school math-inclined students to its STEM classes, also boasted of many alumni among the top scorers, including Zhu and 15 other MIT students, and three Harvard students — including a "next-12" finisher, Franklyn Wang, and an Elizabeth Lowell Putnam co-winner Laura Pierson.</p> <p>Many MIT Putnam competitors have prepared for the exam by participating in the first-year Putnam Seminar <a href="">18.A34 (Mathematical Problem Solving, Putnam Seminar)</a>, taught by Zhao, who was a three-time Putnam Fellow when he was an undergraduate at MIT. Through the seminar, Zhao encourages students to “use their experience in math competitions as a springboard onto higher mathematics,” and emphasizes the importance of good communication and presentation skills.</p> <p>A number of Putnam competitors go on to have successful research careers. Several faculty members of the Department of&nbsp;Mathematics were Putnam Fellows: Davesh Maulik, Bjorn Poonen, Peter Shor, David Vogan, and Zhao. In <a href="">Putnam’s history</a>, only eight participants were four-time Putnam Fellows, including Poonen, and three of them were MIT students. In fact, the first four-time Putnam Fellow was former MIT student Don Coppersmith '72, who went on to have a successful research career in cryptography.</p> <p>Success at math competitions “is neither necessary nor sufficient to becoming a good research mathematician,” according to Zhao. Nevertheless, he believes that the skills promoted by math competitions can be useful in research mathematics. Zhao regularly works with MIT undergraduate students to produce <a href="">cutting-edge research results</a>. “I am very fortunate to work with these amazing students,” says Zhao.</p> <p>Administered by the Mathematical Association of America, the competition included 150 MIT students among 4,229 test-takers from 570 U.S. and Canadian institutions. The six-hour exam, taken over two sessions on the first Saturday of December each year, consists of 12 problems worth 10 points each. Fewer than a fourth of all participants of this competition scored more than 10 points total, and the median score was 2.</p> <p>Complete results from the competition can be found on the <a href="">MAA website</a>. For more history on the competition, former MAA President Joseph A. Gallian wrote an interesting <a href="">2015 overview</a>.&nbsp;</p> MIT students set records at this year’s Putnam Competition: (left to right) Shengtong Zhang, Yuan Yao, Kevin Sun, Daniel Zhu, Qi Qi, and Dain Kim. Not pictured: Ashwin Sah. Photo: Sandi MillerMathematics, School of Science, Awards, honors and fellowships, Contests and academic competitions, Students, Undergraduate, Women in STEM Machine learning picks out hidden vibrations from earthquake data Technique may help scientists more accurately map vast underground geologic structures. Fri, 28 Feb 2020 13:00:46 -0500 Jennifer Chu | MIT News Office <p>Over the last century, scientists have developed methods to map the structures within the Earth’s crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns. The way these waves bounce and scatter through the Earth can give scientists an idea of the type of structures that lie beneath the surface.</p> <p>There is a narrow range of seismic waves — those that occur at low frequencies of around 1 hertz — that could give scientists the clearest picture of underground structures spanning wide distances. But these waves are often drowned out by Earth’s noisy seismic hum, and are therefore difficult to pick up with current detectors. Specifically generating low-frequency waves would require pumping in enormous amounts of energy. For these reasons, low-frequency seismic waves have largely gone missing in human-generated seismic data.</p> <p>Now MIT researchers have come up with a machine learning workaround to fill in this gap.</p> <p>In a paper appearing in the journal <em>Geophysics</em>, they describe a method in which they trained a neural network on hundreds of different simulated earthquakes. When the researchers presented the trained network with only the high-frequency seismic waves produced from a new simulated earthquake, the neural network was able to imitate the physics of wave propagation and accurately estimate the quake’s missing low-frequency waves.</p> <p>The new method could allow researchers to artificially synthesize the low-frequency waves that are hidden in seismic data, which can then be used to more accurately map the Earth’s internal structures.</p> <p>“The ultimate dream is to be able to map the whole subsurface, and be able to say, for instance, ‘this is exactly what it looks like underneath Iceland, so now you know where to explore for geothermal sources,’” says co-author Laurent Demanet, professor of applied mathematics at MIT. “Now we’ve shown that deep learning offers a solution to be able to fill in these missing frequencies.”</p> <p>Demanet’s co-author is lead author Hongyu Sun, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences.</p> <p><strong>Speaking another frequency</strong></p> <p>A neural network is a set of algorithms modeled loosely after the neural workings of the human brain. The algorithms are designed to recognize patterns in data that are fed into the network, and to cluster these data into categories, or labels. A common example of a neural network involves visual processing; the model is trained to classify an image as either a cat or a dog, based on the patterns it recognizes between thousands of images that are specifically labeled as cats, dogs, and other objects.</p> <p>Sun and Demanet adapted a neural network for signal processing, specifically, to recognize patterns in seismic data. They reasoned that if a neural network was fed enough examples of earthquakes, and the ways in which the resulting high- and low-frequency seismic waves travel through a particular composition of the Earth, the network should be able to, as they write in their paper, “mine the hidden correlations among different frequency components” and extrapolate any missing frequencies if the network were only given an earthquake’s partial seismic profile.</p> <p>The researchers looked to train a convolutional neural network, or CNN, a class of deep neural networks that is often used to analyze visual information. A CNN very generally consists of an input and output layer, and multiple hidden layers between, that process inputs to identify correlations between them.</p> <p>Among their many applications, CNNs have been used as a means of generating visual or auditory “deepfakes” — content that has been extrapolated or manipulated through deep-learning and neural networks, to make it seem, for example, as if a woman were talking with a man’s voice.</p> <p>“If a network has seen enough examples of how to take a male voice and transform it into a female voice or vice versa, you can create a sophisticated box to do that,” Demanet says. “Whereas here we make the Earth speak another frequency — one that didn’t originally go through it.”</p> <p><strong>Tracking waves</strong></p> <p>The researchers trained their neural network with inputs that they generated using the Marmousi model, a complex two-dimensional geophysical model that simulates the way seismic waves travel through geological structures of varying density and composition. &nbsp;</p> <p>In their study, the team used the model to simulate nine “virtual Earths,” each with a different subsurface composition. For each Earth model, they simulated 30 different earthquakes, all with the same strength, but different starting locations. In total, the researchers generated hundreds of different seismic scenarios. They fed the information from almost all of these simulations into their neural network and let the network find correlations between seismic signals.</p> <p>After the training session, the team introduced to the neural network a new earthquake that they simulated in the Earth model but did not include in the original training data. They only included the high-frequency part of the earthquake’s seismic activity, in hopes that the neural network learned enough from the training data to be able to infer the missing low-frequency signals from the new input.</p> <p>They found that the neural network produced the same low-frequency values that the Marmousi model originally simulated.</p> <p>“The results are fairly good,” Demanet says. “It’s impressive to see how far the network can extrapolate to the missing frequencies.”</p> <p>As with all neural networks, the method has its limitations. Specifically, the neural network is only as good as the data that are fed into it. If a new input is wildly different from the bulk of a network’s training data, there’s no guarantee that the output will be accurate. To contend with this limitation, the researchers say they plan to introduce a wider variety of data to the neural network, such as earthquakes of different strengths, as well as subsurfaces of more varied composition.</p> <p>As they improve the neural network’s predictions, the team hopes to be able to use the method to extrapolate low-frequency signals from actual seismic data, which can then be plugged into seismic models to more accurately map the geological structures below the Earth’s surface. The low frequencies, in particular, are a key ingredient for solving the big puzzle of finding the correct physical model.</p> <p>“Using this neural network will help us find the missing frequencies to ultimately improve the subsurface image and find the composition of the Earth,” Demanet says.</p> <p>This research was supported, in part, by Total SA and the U.S. Air Force Office of Scientific Research.</p> MIT researchers have used a neural network to identify low-frequency seismic waves hidden in earthquake data. The technique may help scientists more accurately map the Earth’s interior.Image: Christine Daniloff, MITEAPS, Earthquakes, Environment, Geology, Mathematics, Research, School of Science, Machine learning, Artificial intelligence, Earth and atmospheric sciences It all adds up MIT students train teams in Ghana and Uganda for the International Mathematical Olympiad through MISTI-Africa. Wed, 26 Feb 2020 14:45:01 -0500 Laura Carter | School of Science <p>The <a href="" target="_blank">International Mathematical Olympiad</a> (IMO) is more than a math competition for high schoolers: It’s also a springboard for subsequent success. The MIT delegation that annually <a href="" target="_blank">dominates the Putnam Mathematical Competition</a> is largely composed of alumni of the IMO and related math competitions. Many of these mathletes remain involved by producing training or prep courses and study guides, like the popular <em>Euclidean Geometry in Mathematical Olympiads</em> by Evan Chen ’18, a PhD student in the Department of Mathematics<em>, </em>which is read by aspiring contestants around the world.</p> <p>Now, a new program invites MIT undergraduates, particularly those with a background in competition mathematics, to travel across the globe to train the national teams in Uganda and Ghana.</p> <p>“MIT is a magnet for talent,” says Chris Peterson, a senior assistant director in the MIT Admissions Office. Enabling alumni to assist the next generation of competitors factors into MIT’s campaign of creating a better world. “I think anything we can do to help spread the intellectual wealth concentrated at MIT, while giving our students a global education, is a win-win,” Peterson says.</p> <p>The goal is not just about helping African teams to place at the competition. A <a href="" target="_blank">recent study</a> by economists at the International Monetary Fund and University of Bath suggests that the skills honed by math contests help contribute to mathematical productivity and economic prosperity down the line. “It helps to identify and develop a critical mass of problem-solvers who will help develop the world,” explains Joel Dogoe, who founded the Mawuenyega International Science and Engineering (MISE) educational non-profit program in Ghana that recruits and trains the country’s IMO team and <a href="" target="_blank">has collaborated with MIT before</a>.</p> <p><strong>One plus one</strong></p> <p>In the summer of 2019, Dogoe worked with Ari Jacobovits in the MIT International Science and Technology Institute (MISTI) <a href="" target="_blank">Africa program</a> to fly three MIT students to Ghana for the summer. After working with the MIT students, the Ghanaian team received honorable mention at the 2019 IMO — the first award they have received in their five years of participation.</p> <p>“I couldn’t believe it after only a single joint program,” says Jacobovits. “It became clear that we needed to scale up and get organized. Now my focus has extended to working with our partners to bring an IMO medal to an African country. The talent is there, and it would mean so much not just for the country, but for the whole world to see.”<br /> <br /> Part of that “getting organized” meant securing funding, which had not been established for that trial run. That’s when MIT’s <a href="" target="_blank">Department of Mathematics</a> stepped in to offer support, and the program was able to build up its numbers.</p> <p>"We are fortunate at MIT to have students who are not only mathematically brilliant but also care about helping others develop their passion for mathematics," said Professor Michel Goemans, head of the Department of Mathematics. "It is an amazing experience for both the MIT students and these students from Ghana and Uganda. This program provides the talented students from these African nations the opportunities and mathematical resources that they would not otherwise easily have access to."</p> <p><strong>Raising the total</strong></p> <p>This January, during the MIT Independent Activities Period (IAP), three more MIT students, Andrew Gu, Eshaan Nichani, and Carolina Ortega, flew to Ghana and an additional three, Sean Elliot, Violet Felt, and Michael Ren, made their way to Uganda.</p> <p>The students spent three weeks training the local African IMO teams and organized STEM outreach with local school visits. Eshaan Nichani, a senior double majoring in Mathematics and Computer Science and Engineering, explains that in Ghana, they spent the first week at the IMO training camp and the second week touring 11 middle and high schools to discuss college in the United States, MIT, and mathematics.</p> <p>“The three letters M – I – T bring a lot of excitement to science and math enthusiasts all over the world and Ghana is no exception,” says Dogoe. “In some schools, it was difficult to leave because the [Ghanaian] students kept engaging the MIT students well into the night.” Nichani recalls one student, who showed off his homemade generator, told him that MIT is his dream school.</p> <p>The largest hurdle Sean Elliott, a first-year in Course 18 (mathematics), encountered in Uganda was providing the Olympiad students with challenging enough problems to satiate their curiosity. Elliott, who attended the elite Mathematical Olympiad Summer Program, which trains students for the American IMO team, joined the MIT community because of its passion for STEM and strong culture of collaboration.</p> <p>“One thing that became clear while working with these students is that they have similar levels of talent in math compared to students in the U.S.,” says Michael Ren, another MIT Course 18 student who earned a gold medal at the IMO in 2018 and is in his second year at MIT, but their abilities and passion for math are limited by their lack of access to resources.</p> <p>Violet Felt, a third-year student majoring in electrical engineering and computer science, agreed. “It was a surreal adventure: We were teaching complex graph theory and proof techniques in an open-air classroom with one blackboard, no WiFi, no electricity,” she says, “but the same kind of smart minds you find every day at MIT.”</p> <p>Professor Hazel Sive, faculty director for MISTI-Africa and director of the <a href="">MIT-Africa Initiative</a>, visited the Uganda program. “This is a fantastic contribution by the MIT Mathematics Department. Our students ran an outstanding program for the best high school math talent in Uganda. The Ugandan students were exceptional, and we hope some will be attracted to apply to MIT.” &nbsp;</p> <p>Sive, also a professor in the <a href="">Department of Biology</a> and member of the <a href="">Whitehead Institute</a>, points out that the goals of MIT-Africa are to “promote mutually beneficial interactions between MIT and African collaborators.” She adds, “This program is a wonderful example — our students were enriched by experiencing cultures new to them, and top African students were enriched by the training our students were able to share.” &nbsp;</p> <p><strong>Coming first</strong></p> <p>For many of the MIT students, the camp was also a unique chance to improve their teaching skills, especially in a different setting than on campus — an important trait to develop for their potential careers in academia. Because the partnership is so new, the lessons, handouts, and lectures were all generated by the student trainers. They stayed up late to grade the exams of as many as 70 students in the case of the Uganda team. This year’s IAP travelers laid the groundwork for a consistent training structure and schedule.</p> <p>In addition to a teaching opportunity, the program also provides a broader view of the world from a new perspective. Several of the trainers mutually declared their favorite moments of the trip being the times they learned about lifestyle differences and similarities between Americans and Ghanaians. They recalled in one presentation in particular that involved a discussion about prom – with a lot of laughter.</p> <p>Although these students will have to wait until the summer to hear how their pupils perform at the <a href="">2020 IMO competition</a>, which will take place in Russia in July, the overall feeling of the program is one of success. Ren pointed out that after their return to MIT, several Ugandan students messaged them in appreciation, one of whom admitted that he came to the camp for the socialization and stayed for the math.</p> <p><strong>More to go</strong></p> <p>The students, organizers, and participants equally expressed hope that this partnership continues and grows. “Personally, I would love to see this program expand, both to more countries in mathematics and in other fields such as physics, chemistry, and biology,” says Peterson. These fields have analogue competitions, each with their own networks of alumni. “I could easily imagine a future where every January, dozens of MIT undergrads with Olympiad experience deploy all over the world to help share their knowledge with, and bring back insights and experiences from, like minded students in other countries.”</p> <p>When asked if he would participate in the MISTI-Africa IMO program again, Elliott responded with a resounding “Yes!” and all recommended the experience to other students. By Ortega’s reasoning, it provides an unique opportunity to share a passion for math and encourage students, as well as dispel existing math stereotypes. For her, “it has always been very important to think of what I can do with my knowledge for others,” says the mathematics third-year and two-time former Colombian IMO team member.</p> <p>As Peterson notes, “This is really such an incredible opportunity to mix global education with mission-driven service in a way that few schools are as well-positioned as MIT to provide.”</p> Carolina Ortega, a third-year mathematics student at MIT, talks with students as part of a MISTI-Africa program to connect MIT mathletes with their Ghanaian counterparts.Image: Joel DogoeStudent life, Mathematics, Contests and academic competitions, MISTI, Africa, Independent Activities Period, Students, Undergraduate, STEM education, K-12 education, School of Science, Classes and programs Four MIT researchers elected to the National Academy of Engineering for 2020 New members have made advances in computer architecture, network coding, ocean engineering, higher education, and quantum computation. Wed, 26 Feb 2020 11:40:01 -0500 School of Engineering | School of Science | MIT Schwarzman College of Computing <p>Four MIT researchers are among the 87 new members and 18 foreign associates <a href="" target="_blank">elected to the&nbsp;National Academy of Engineering</a> for 2020.</p> <p>Election to the National Academy of Engineering is among the highest professional distinctions accorded to an engineer. Academy membership honors those who have made outstanding contributions to "engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature," and to "the pioneering of new and developing fields of&nbsp;technology, making major advancements in traditional fields of engineering, or&nbsp;developing/implementing innovative approaches to engineering education.”</p> <p>The four elected this year include:</p> <p><a href="">Joel Emer</a>, professor of the practice in the Department of Electrical Engineering and Computer Science, for quantitative analysis of computer architecture and its application to architectural innovation in commercial microprocessors.</p> <p><a href="" target="_blank">Muriel Médard</a>, the Cecil H. Green Professor of Electrical Engineering and Computer Science, for contributions to the theory and practice of network coding.</p> <p><a href="">Peter Shor</a>, the Morss Professor of Applied Mathematics, for pioneering contributions to quantum computation.</p> <p><a href="">Dick K.P. Yue</a>, the Philip J. Solondz Professor of Engineering and professor of mechanical and ocean engineering, for contributions to ocean engineering and innovation of OpenCourseWare to make higher education freely available worldwide.</p> <p>Including this year’s inductees, 142 members of the NAE are current or retired members of the MIT faculty and staff, or members of the MIT Corporation.</p> Four MIT researchers are among the 87 new members and 18 foreign associates elected to the National Academy of Engineering. Image courtesy of the National Academy of EngineeringFaculty, Awards, honors and fellowships, Mathematics, Electrical Engineering & Computer Science (eecs), School of Engineering, School of Science, MIT Schwarzman College of Computing, Mechanical engineering Michael Sipser to step down as School of Science dean Mathematician to return to the faculty after six years leading MIT’s second-largest school. Wed, 19 Feb 2020 15:24:00 -0500 Steve Bradt | MIT News Office <p>Michael Sipser plans to step down as dean of the MIT School of Science, concluding six years of service marked by the launch of key initiatives and the upgrading of facilities across the school’s six academic departments.</p> <p>Provost Martin Schmidt announced the news today in an email to the MIT community. Following Sipser’s service as dean — which will conclude on June 30, assuming that a suitable successor is found by then — he will return to the faculty, where he is the Donner Professor of Mathematics.</p> <p>“Mike’s accomplishments as dean span the School of Science and have built its strength in both research and education, often by increasing the impact of science on critical areas of collaborative study,” Schmidt wrote.</p> <p>With 280 faculty, the School of Science is MIT’s second-largest school. It comprises the departments of Biology; Brain and Cognitive Sciences; Chemistry; Earth, Atmospheric and Planetary sciences; Mathematics; and Physics.</p> <p>“Three qualities have defined Mike’s outstanding service as dean: his thoughtful, patient, evenhanded approach to complex organizational and human issues; his wonderful ability to explain, advocate for, and share his infectious pleasure in the scientific work of others; and his absolutely delightful sense of humor,” President L. Rafael Reif says. “MIT and the School of Science have been extremely fortunate to have Mike’s leadership, our students have benefited immeasurably from his commitment to teaching throughout his deanship — and I can attest to how much he has taught me personally about the frontiers of scientific knowledge.”</p> <p>Sipser, a leading theoretical computer scientist, was named dean of science in June 2014, following six months as interim dean. Prior to that, he had served since 2004 as head of the Department of Mathematics.</p> <p>“I’m most pleased that I enabled the work of our community in the School of Science — faculty, staff, and students — through providing resources, facilitating progress, removing obstacles, and cheering their successes,” Sipser says.&nbsp;“It has been a great privilege for me to support these amazing colleagues.”</p> <p>Sipser’s key accomplishments as dean have included:</p> <ul> <li>helping to launch the <a href="">Aging Brain Initiative</a>, an interdisciplinary effort centered in the Department of Brain and Cognitive Sciences and the Picower Instutute for Learning and Memory, to understand and develop treatments for age-related neurodegenerative diseases, such as Alzheimer’s and Parkinson’s;</li> <li>championing a home for statistics at MIT through the creation of the <a href="">MIT Statistics and Data Science Center</a> in what is now known as the MIT Institute for Data, Systems, and Society;</li> <li>participating in the design of the <a href="">MIT Quest for Intelligence</a> as an outgrowth of MIT’s <a href="">Center for Brains, Minds, and Machines</a>;&nbsp;&nbsp;</li> <li>working with the Department of Mathematics to sustain its <a href="">MathROOTS</a> program for high-potential high school students from underrepresented and underprivileged backgrounds; &nbsp;</li> <li>facilitating work by the Department of Biology to create a <a href="">Cryo-Electron Microscopy</a> facility in MIT.nano, the Institute’s state-of-the-art nanotechnology research center that opened in 2018;&nbsp;&nbsp;</li> <li>assisting the Department of Chemistry in modernizing its shared <a href="">Instrumentation Facility</a>;</li> <li>helping astronomy faculty in the Department of Earth, Atmospheric and Planetary Sciences purchase a new telescope for the <a href="">Wallace Astrophysical Observatory</a> in Westford, Massachusetts; and</li> <li>working with the Department of Physics and the MIT Kavli Institute for Space Research to secure a National Science Foundation Major Research Instrumentation grant for a wide-field infrared camera.</li> </ul> <p>Sipser has received multiple MIT awards for his teaching and advising. In 2016, while serving as dean, he received the MIT Margaret MacVicar Faculty Fellowship, in recognition of his outstanding commitment to undergraduate education.</p> <p>A fellow of the American Academy of Arts and Sciences, Sipser authored the widely used textbook “Introduction to the Theory of Computation,” first published in 1996. He earned his BA in mathematics from Cornell University in 1974 and his PhD in engineering from the University of California at Berkeley in 1980. He joined MIT’s Laboratory for Computer Science as a research associate in 1979, becoming an assistant professor of applied mathematics in 1980; associate professor of applied mathematics in 1983; and professor of applied mathematics in 1989.</p> <p>In his letter to the community, Schmidt said that he plans to appoint a faculty committee to advise him on the selection of the next dean of science. Members of the MIT community are encouraged to send suggestions and ideas to&nbsp;<a href=""></a>.</p> Michael SipserImage: Justin Knight, edited by MIT NewsAdministration, Faculty, School of Science, Mathematics, President L. Rafael Reif Richard Dudley, professor emeritus of mathematics, dies at 81 Longtime MIT professor strongly influenced the fields of probability, statistics, and machine learning. Tue, 18 Feb 2020 15:25:01 -0500 Department of Mathematics <p>Richard Mansfield Dudley, MIT professor emeritus of mathematics, died on Jan. 19 following a long illness. He was 81. Dudley served on the MIT mathematics faculty from 1967 until 2015, when he officially retired. Over the course of those 48 years, during which he published over 100 articles as well as numerous books and monographs, he made fundamental breakthroughs in the theory of stochastic process and the general theory of weak convergence.</p> <p>Dudley’s work, starting in the 1960s, shaped the fields of probability, mathematical statistics, and machine learning, with highly influential contributions to the theory of Gaussian processes and empirical processes. What is now widely known as “Dudley’s entropy bound” has become a standard tool of modern research in probability, statistics, and machine learning. Dudley’s work also had a transformative impact on the theory of empirical processes initiated by Vladimir Vapnik and Alexey Chervonenkis in the context of machine learning. Over a series of papers, starting with his landmark paper “Central limit theorems for empirical processes” (<em>Annals of Probability</em>, 1978) and culminating with his influential Saint-Flour lecture notes (1984) and later, his book “Uniform Central Limit Theorems”<em> </em>(Cambridge University Press, 1999), Dudley distilled and developed these ideas into an actionable theory that still today is the reference framework in mathematical statistics and statistical learning theory. The larger communities of probability and statistics remember his excellent taste for mathematically rich and impactful subjects, as well as his highest standard of rigor.</p> <p>Dudley gave a number of distinguished research talks. He was an invited speaker at the 1974 International Congress of Mathematicians as well as at meetings of the American Mathematical Society, the Institute of Mathematical Statistics, and the Bernoulli society. He was also an invited lecturer at Saint-Flour probability summer school in probability in 1982 and several of the Vilnius Conferences on Probability Theory and Mathematical Statistics. He was a regular participant and organizer of several conferences and meetings, including Probability in Banach Spaces.</p> <p>In 1976, Dudley visited the University of Aarhus, and there produced a set of graduate lecture notes, “Probabilities and Metrics.” These were to become a part of his graduate text, “Real Analysis and Probability,”<em> </em>published by Wadsworth, Inc. in 1989. An early review of this work in the <em>London Mathematical Society Bulletin</em> (July 1990) found that it “could be compared to the appearance of Breiman or Loève's classic probability texts.” The text has since become a standard, and in 2002 was reissued by Cambridge University Press and continues to be in print.</p> <p>Dudley was always highly regarded as a graduate mentor throughout his career. He advised 33 PhD candidates (32<em> </em>at MIT), yielding some 105 academic “descendants.”</p> <p>Dudley served the scholarly community as associate editor (1972-78) and then chief editor (1979-81) of <em>Annals of Probability</em>. He was a member of the editorial board of the <em>Wadsworth/Brooks/Cole Advanced Series in Statistics/Probability </em>from 1982 to 1992. For many years while on the MIT faculty, Dudley worked with the MIT Science Library in overseeing their collection of mathematical journals. He sought to explain to the faculty how the library's budget decisions were reached, to help them effectively express their research needs.</p> <p>Among his honors, Dudley was an Alfred P. Sloan Research fellow from 1966-68 and Guggenheim Foundation Fellow in 1991. He was selected to serve on the honorary Advisory Board of Stochastic Processes and their Applications from 1987-2001. In 1993, Dudley was elected a fellow of the American Statistical Association, “for world-recognized contributions to probability theory with far-reaching consequences for statistics, for founding the modern theory of empirical processes, and for dedication to many successful PhD students.” He was also elected fellow of the Institute of Mathematical Statistics, the American Association for the<em> </em>Advancement of Science, and the American Mathematical Society and was selected to be a member of the International Statistical Institute.</p> <p>Born on July 28, 1938, in Cleveland, Ohio, Dudley completed a BA from Harvard University, summa cum laude, in 1959. He wrote a doctoral dissertation under two advisors at Princeton University, Gilbert A. Hunt and Edward Nelson, completing his PhD in mathematics in 1962. He was an instructor at the University of California at Berkley in 1962-63, and an assistant professor from 1963 to 1967, before moving to MIT.</p> <p>Dudley is survived by his wife, Elizabeth (Liza) Martin; his sisters, Edith D. Sylla and Alice D. Carmel; brother-in-law Richard E. Sylla; and nieces Anne Sylla, Margaret S. Padua, and Genevieve Carmel.&nbsp;</p> <p>Memorial contributions may be made in Dudley's name to the <a href="" target="_blank">Environmental Defense Fund</a> or to <a href="" target="_blank">Partners in Health</a>.</p> Richard M. Dudley taught mathematics at MIT for 48 years, until he retired in 2015.Image courtesy of the Department of Mathematics.Mathematics, Obituaries, Faculty, School of Science, Machine learning, Education, teaching, academics New theories at the intersection of algebra and geometry Professor Chenyang Xu applies the techniques of abstract algebra to study concrete but complex geometric objects. Sat, 08 Feb 2020 23:59:55 -0500 Jonathan Mingle | MIT News correspondent <p>As a self-described “classical type of mathematician,” Chenyang Xu eschews software for paper and pen, chalk and chalkboard. Walk by his office, and you might simply see him pacing about, deep in concentration.</p> <p>Walking — across campus to get a cup of coffee, or from his apartment to his office — is an essential part of his process.</p> <p>“The way I think about math, I do a lot of picturing in my brain,” he says. “If I need a more clear picture, I might draw something and do some calculations. And when I walk I think of these pictures.”</p> <p>Those paces sometimes lead him to colleagues’ offices. “There are so many great minds here, and I interact with my colleagues in the department a lot,” says Xu, a recently tenured professor of mathematics at MIT.</p> <p>Xu’s specialty is algebraic geometry, which applies the problem-solving methods of abstract algebra to the complex but concrete shapes, surfaces, spaces, and curves of geometry. His primary objects of study are algebraic varieties — geometric manifestations of sets of solutions of systems of polynomial equations. As he walks and talks with colleagues, Xu focuses on ways of classifying these algebraic varieties in higher dimensions, using the techniques of birational geometry.</p> <p>“I like to talk with other mathematicians working in my subject,” Xu says. “We discuss a bit, then go back to think for ourselves, encounter new difficulties, then discuss again. Most of my papers are basically collaborations.”</p> <p>Such a collaboration helped Xu take his research in a new direction toward developing the new theory of K-stability of Fano varieties. Eight years ago, he devoted some thought to a certain subject in his field known as K-stability, which he describes as “an algebraic definition invented for differential geometry studies.”</p> <p>“I tried to develop an algebraic theory based on this K-stability as a background intuition, using algebraic geometry tools.” After a few years’ “gap,” he eventually came back to it because of conversations with his collaborator Chi Li, a professor of mathematics at Purdue University.</p> <p>“He had more of a differential geometry background and translated that concept into algebraic geometry,” says Xu. “That’s when I realized this was important to study. Since then, we have done more than we expected four or five years ago.”</p> <p>Together they published a highly cited <a href="">paper</a> in 2014 on the “K-stability of Fano varieties,” which put forward an entirely new theory in the field of birational algebraic geometry.</p> <p>It was representative of his approach to mathematics, which involves advancing new theories before tackling specific problems.</p> <p>“In my subject there are questions that everybody trying to solve, that have been open for 40 years,” Xu says. “I have those kinds of problems in my mind. My way of doing math is to go after the theory. Instead of working on one problem with techniques, we have to first develop the theory. We then see something in a new light. Every time I find some new theory, I test it on old classical problems to see if it works or not.”</p> <p><strong>The beauty of math</strong></p> <p>Growing up near Chengdu, in China’s Sichuan Province, Xu enjoyed math from a young age. “I attended some math Olympiads, and I did okay, but I wasn’t the gold medal winner,” he says with a laugh.</p> <p>He was talented enough, however, to earn bachelor’s and master’s degrees at Peking University, as a part of the premier math program in China.</p> <p>“After I got into college, I started to learn more advanced mathematics, and I found it very beautiful and very deep,” he says. “To me, a big chunk of mathematics is art more than science.”</p> <p>Toward the end of his time at Peking, he concentrated increasingly on algebraic geometry. “I just like geometry a lot and wanted to study some subject related to geometry,” he says. “I found that I’m good at the techniques of algebra. So using those techniques to study geometry fit me very well.”</p> <p>Xu then pursued a PhD at Princeton University, where his advisor, János Kollár, a leading algebraic geometer, had a “huge influence” on him.</p> <p>“What I learned from him, aside from many techniques, of course, was more about what I could call ‘taste,’” says Xu. “What questions are important in mathematics? In general, graduate students or postdocs in the early stages of their career need some role model to follow. Doing math is a complicated thing, and at some point there are choices they need to make,” he says, that require balancing how difficult or interesting a particular problem might be with more practical concerns about its tractability.</p> <p>In addition to Kollár’s mentorship, the unfamiliarity of his new surroundings also aided his research.</p> <p>“I had never been outside China before that point, so there was a bit of culture shock,” he recalls. “I didn’t know much about U.S. culture at the time. But in some sense that made me even more concentrated on my work.”</p> <p>After Xu received his doctorate in 2008, he spent three years as a postdoc and C.L.E. Moore Instructor at MIT. He then spent about six years as a professor at the Beijing International Center of Mathematical Research and then returned to MIT as a full professor of mathematics in 2018.</p> <p>Throughout those years, Xu demonstrated a talent for finding important questions to pursue, becoming a leading thinker in his field and making a series of major advances in algebraic birational geometry.</p> <p>In 2017, Xu won the inaugural Future Science Prize in Mathematics and Computer Science for his “fundamental contributions” to the field of birational geometry. Some of that field’s real-world applications include coding and robotics. For example, birational geometry techniques are used to help robots “see” by grouping a series of two-dimensional pictures together into something approximating a field of vision to navigate our three-dimensional world.</p> <p>Xu’s work to advance the minimal model program (MMP) — a key theory in birational geometry that was first articulated in the early 1980s — and apply it to algebraic varieties won him the 2019 New Horizons Prize for early-career achievement in mathematics. He has since proved a series of conjectures related to the MMP, expanding it to previously untested varieties of certain conditions.</p> <p>The theory of algebraic K-stability that he developed has proven to be fertile ground for new discoveries. “I’m still working on this topic, and it’s a particularly interesting question to me,” he says.</p> <p>Xu has been making progress on proving other key conjectures related to K-stability rooted in the minimal model program. Recently, he drew on that prior work to prove the existence of moduli space for Fano algebraic varieties. Now he’s hard at work developing a solution for a specific property of that moduli space: its “compactness.”</p> <p>“To solve that problem it will be very important,” he says. “I hope we can still solve the last piece of it. I’m pretty sure that would be my best work to date.”</p> Chenyang XuImage: M. Scott BrauerProfile, Faculty, Mathematics, School of Science, China Mehtaab Sawhney named 2020 Churchill Scholar MIT senior will pursue graduate studies in mathematics at Churchill College, Cambridge University. Thu, 30 Jan 2020 10:00:01 -0500 Julia Mongo | Office of Distinguished Fellowships <p>Mehtaab Sawhney, a senior from Commack, New York, has been named a 2020 Churchill Scholar and will pursue a year of graduate studies at Cambridge University in the U.K. Sawhney will graduate this February with a BS in mathematics and a minor in computer science. At Cambridge, he will undertake Part III of the Mathematics Tripos master’s degree before returning to the U.S. to enroll in a mathematics PhD program. He aspires to become a professor of mathematics specializing in combinatorics.</p> <p>Sawhney completed his first year of undergraduate studies at the University of Pennsylvania and then transferred to MIT. At MIT, he has contributed to more than a dozen published or submitted academic papers, a rare feat for an undergraduate student. The majority of his research has been done in combinatorics under the tutelage of Professor Yufei Zhao in the MIT Department of Mathematics.</p> <p>“Mehtaab is an incredibly talented and energetic mathematician,” states Zhao. “I constantly learn so much from talking to him. Working with Mehtaab on research has been one of the most fun and rewarding activities that I have done since joining MIT as a faculty member.”</p> <p>Sawhney began his impressive rise in mathematics in high school, where he was a participant in the United States Mathematical Olympiad. He found the activity of solving problems fascinating. In high school, he got his first real taste of research through the MIT Primes-USA Program, which pairs high school students with graduate students to solve problems collectively but remotely. Here he first encountered combinatorics, an area of mathematics that focuses on counting.</p> <p>Sawhney continued to work on math problems in the Math Olympiad, International Science and Engineering Fair, and then eventually the Putnam Mathematical Competition (where he was an honorable mention in both 2016 and 2018). He volunteers his time with the U.S. Mathematical Olympiad and the U.S. Team Selection Test as a grader and reviewer.</p> <p>The Churchill Scholarship provides funding to American students for a year of master’s study at Cambridge University, based at Churchill College. The program was set up at the request of former British Prime Minister Winston Churchill to honor his vision of U.S.-U.K. scientific exchange. The Churchill Foundation annually awards scholarships to 15 American students for study in science, mathematics, or engineering. MIT nominates two candidates each year. MIT students interested in learning more about applying for the Churchill Scholarship, and other distinguished fellowships, should contact Kimberly Benard, assistant dean of Career Advising and Professional Development.</p> Mehtaab Sawnhey is a 2020 Churchill Scholar.Photo courtesy of Mehtaab SawnheyAwards, honors and fellowships, Students, Mathematics, School of Science Accelerating the pace of engineering The 2019-20 School of Engineering MathWorks Fellows are using MATLAB and Simulink to advance discovery and innovation across disciplines. Tue, 28 Jan 2020 17:00:01 -0500 Lori LoTurco | School of Engineering <p>Founded in 1984 by Jack Little ’78 and Cleve Moler, MathWorks was built on the premise of providing engineers and scientists with more powerful and productive computation environments. In 1985, the company sold its very first order&nbsp;— 10 copies of its first product, MATLAB — to MIT.</p> <p>Decades later, engineers across MIT and around the world consistently rely on MathWorks products to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, biotech-pharmaceutical, and other industries.&nbsp;MathWorks’ products and support have had a significant impact on <em>MITx,</em> OpenCourseWare, and MIT’s digital learning efforts across campus, including the Department of Mathematics, one of the School of Engineering’s closest collaborators in the use of digital learning tools and educational technologies.</p> <p>“We have a strong belief in the importance of engineers and scientists,” says Little. “They act to increase human knowledge and profoundly improve our standard of living. We create products like MATLAB and Simulink to help them do their best work.”</p> <p>As the language of technical computing, MATLAB is a programming environment for algorithm development, data analysis, visualization, and numeric computation. It is used extensively by faculty, students, and researchers across MIT and by over 4 million users in industry, government, and academia in 185 countries.</p> <p>Simulink is a block diagram environment for simulation and model-based design of multidomain and embedded engineering systems, including automatic code generation, verification, and validation. It is used heavily in automotive, aerospace, and other applications that design complex real-time systems.</p> <p>This past summer, MathWorks celebrated 35 years of accelerating the pace of engineering and science. Shortly following this milestone, MathWorks awarded 11 engineering fellowships to graduate students within the School of Engineering who are active users of MATLAB or Simulink. The fellows are using the programs to advance discovery and innovation across disciplines.</p> <p>“PhD fellowships are an investment in the world’s long-term future, and there are few investments more valuable than that,” says Little.</p> <p>The 2019-20 MathWorks fellows are:</p> <p><a href="">Pasquale Antonante</a> is a PhD student in the Department of Aeronautics and Astronautics. He uses MATLAB and Simulink to build tools that make robots more accurate.</p> <p><a href="">Alireza Fallah</a> is a PhD student in the Department of Electrical Engineering and Computer Science. He uses Matlab and Symbolic Math Toolbox to develop better machine-learning algorithms.</p> <p><a href="">James Gabbard</a> is a SM/PhD student in the Department of Mechanical Engineering. He uses MATLAB to model fluids and materials.</p> <p><a href="">Nicolas Meirhaeghe</a><strong> </strong>is a PhD student in medical engineering and medical physics in the Bioastronautics Training Program at Harvard-MIT Division of Health Sciences and Technology. He uses MATLAB to visualize activity in the brain and understand how it is related to an individual’s behavior.</p> <p><a href="">Caroline Nielsen</a> is a PhD student in the Department of Chemical Engineering. She uses MATLAB to implement and test new applications of non-smooth analysis. She also intends to use MATLAB to in the next phase of her research, developing methods to simultaneously optimize for minimal resource use and operating costs.</p> <p><a href="">Bauyrzhan Primkulov</a><strong> </strong>is a PhD student in the Department of Civil and Environmental Engineering. He uses MATLAB to build computational models and explore how fluids interact in porous materials.</p> <p><a href="">Kate Reidy</a><strong> </strong>is a PhD student in the Department of Materials Science and Engineering. She studies how 2D materials — only a single atom thick — can be combined with 3D materials, and uses MATLAB to analyze the properties of different materials.</p> <p><a href="">Isabelle Su</a><strong> </strong>is a PhD student in civil and environmental engineering. She builds computational models with MATLAB to understand the mechanical properties of spider webs.</p> <p><a href="">Joy Zeng</a><strong> </strong>is a PhD student in chemical engineering. Her research is focused on the electrochemical transformation of carbon dioxide to fuels and commodity chemicals. She uses MATLAB to model chemical reactions.</p> <p><a href="">Benjamin "Jiahong" Zhang</a><strong> </strong>is a PhD student in computational science and engineering. He uses MATLAB to prototype new methods for rare event simulation, finding new methods by leveraging mathematical principles used in proofs and re-purposing them for computation.</p> <p><a href="">Paul Zhang</a><strong> </strong>is a PhD student in electrical engineering and computer science. He uses MATLAB to develop algorithms with applications in meshing — the use of simple shapes to study complex ones.</p> <p>For MathWorks, fostering engineering education is a priority, so when deciding where to focus philanthropic support, MIT — its very first customer — was an obvious choice.</p> <p>“We are so humbled by MathWorks' generosity, and their continued support of our engineering students through these fellowships,” says Anantha Chandrakasan, dean of the School of Engineering. “Our relationship with MathWorks is one that we revere — they have developed products that foster research and advancement across many disciplines, and through their support our students launch discoveries and innovation that align with MathWorks’ mission.”</p> MathWorks fellows with Anantha Chandrakasan (back row, center), dean of the MIT School of Engineering. Not pictured: Fellows Pasquale Antonante, Alireza Fallah, and Kate Reidy.Photo: David DegnerSchool of Engineering, MITx, OpenCourseWare, Mathematics, Electrical engineering and computer science (EECS), Mechanical engineering, Chemical engineering, Civil and environmental engineering, Awards, honors and fellowships, Harvard-MIT Health Sciences and Technology, Alumni/ae, Startups, Aeronautical and astronautical engineering, DMSE, Computer science and technology, School of Science Finding solutions amidst fractal uncertainty and quantum chaos Math professor Semyon Dyatlov explores the relationship between classical and quantum physics. Sat, 25 Jan 2020 23:59:59 -0500 Jonathan Mingle | MIT News correspondent <p>Semyon Dyatlov calls himself a “mathematical physicist.”</p> <p>He’s an associate editor of the journal <em>Probability and Mathematical Physics. </em>His PhD dissertation advanced understanding of wave decay in black hole spacetimes. And much of his research focuses on developing new ways to understand the correspondence between classical physics (which describes light as rays that travel in straight lines and bounce off surfaces) and quantum systems (wherein light has wave-particle duality).</p> <p>So it may come as a surprise that, as a student growing up in Siberia, he didn’t study physics in depth.</p> <p>“Much of my work is deeply related to physics, even though I didn’t receive that much physics education as a student,” he says. “It took when I started working as a mathematician to slowly start understanding things like general relativity and modern particle physics.”</p> <p><strong>A math-loving family, and inspiring mentors</strong></p> <p>His mathematical education, however, has been extensive — and started early.</p> <p>Dyatlov was raised in a family of mathematicians. One of his two brothers is an applied mathematician. Both of his parents have math degrees. He grew up a five-minute walk away from the campus of Novosibirsk State University (NSU), a major academic research center in Siberia, where his father still teaches.</p> <p>“From a young age I was exposed to all kinds of mathematics,” he says. “There were journals and books lying around our house. I was very lucky that I both liked mathematics and was born into a family where a lot of mathematics was going on.”</p> <p>He can even trace his interest in microlocal analysis — his field of specialty today as an associate professor of mathematics at MIT — to conversations with his older brother decades ago. These talks sparked a fascination with partial differential equations, which Dyatlov studied as an undergraduate at NSU, where both his brother and father received their PhDs.</p> <p>Dyatlov went on to pursue graduate studies at the University of California at Berkeley. There his trajectory was influenced by a course he took during his first year with Professor Maciej Zworski on the theory of scattering resonances, which he explains are “pure states for systems in which energy can scatter to infinity.”</p> <p>It would prove to be a fruitful encounter. Zworski became Dyatlov’s dissertation advisor; a decade later, they are still collaborating. In addition to the many papers that they have written together, they co-authored a new textbook published by the American Mathematical Society in September.</p> <p>Zworski, who received both his bachelor’s degree and PhD in math from MIT, gave Dyatlov a particular problem to tackle early in his graduate studies.</p> <p>“There was back then a bit of a mystery surrounding how to apply scattering theory methods to black holes,” he recalls. The problem, which related to this mystery, grew into his dissertation’s detailed exploration of exponential wave decay in the context of general relativity.</p> <p><strong>Of luck, </strong><strong>collaboration, and “trapped trajectories”</strong></p> <p>In December 2013, Dyatlov began a postdoc at MIT; by 2015 he had been hired as an assistant professor of mathematics. He is now an associate professor and was awarded tenure in 2019.</p> <p>“I sometimes feel I just got lucky many times,” Dyatlov says of his professional journey, from growing up in a family of mathematicians to finding influential mentors and collaborators like Zworski.</p> <p>Dyatlov is now studying how the behavior of quantum systems over long time periods corresponds to that of classical systems. Some of his recent research focuses on spectral gaps for open quantum chaotic systems.</p> <p>To help beginning students conceptualize it, he offers the analogy of striking a bell: “How does the shape of a bell determine how long its sound is sustained?” (Sometimes he uses MIT math department mugs instead.)</p> <p>The shape of the bell determines how long the sound is sustained. The difference lies in both the pitch of the sound, and in how long it can be heard. “You can study both,” he says, “but a natural question to ask is, no matter how you hit the bell, how long does it take for the sound to die out?”</p> <p>Classical physics might characterize what’s happening with the bell (or mug) as a phenomenon similar to light bouncing off a mirror: The sound bounces once off the bell and then escapes to infinity.</p> <p>“Mathematically what you hope to see is some exponential decay of energy, of the solution to a corresponding wave equation,” he explains. What interests Dyatlov is the rate of this decay, and whether, in some situations, there may not be any exponential decay at all.</p> <p>His recent work delves into what happens with these trajectories under conditions of “quantum chaos.”</p> <p>“Say you have waves bouncing off, and everything else escapes but you have a system — say the inside of a bowl — where these classical trajectories never leave. The thing that I study is a situation where you have in your system a fractal set of trapped trajectories,” he says.</p> <p>These trapped trajectories form a fractal set that appears “out of nowhere,” he says. “The fact that fractal sets appear from this was known well before my work, but it was still a surprise to me when I looked at it. Here, a fractal set appears naturally in a problem where you didn’t put in a fractal set.”</p> <p>That work led to his development of what he terms the “fractal uncertainty principle.” The classical uncertainty principle says you can’t pinpoint both the position and momentum of a quantum particle. Dyatlov posited a form of this principle for this fractal set of trapped trajectories.</p> <p><strong>“</strong>I figured out one might be able to solve this wave decay question — this question about partial differential equations, about classical-quantum correspondence, about wave dynamics, and chaotic dynamics — but the component you need is this new kind of fractal uncertainty principle,” he says.</p> <p><strong>Translation and toolboxes</strong></p> <p>Pursuing this question required him to branch out into different fields of math, which lay outside his own training. In that pursuit, he caught another “lucky break:” MIT professor of mathematics Larry Guth suggested he talked with Joshua Zahl, a postdoc who had been thinking independently about a related question, from his own field of additive combinatorics. Applying their respective techniques, they developed a proof for exponential decay in some specific fractal sets and wrote a paper together on the subject. A couple years later — in yet another “lucky” collaboration — Dyatlov worked with the late Jean Bourgain, a renowned mathematician at the Institute for Advanced Study, to prove the fractal uncertainty principle for the general case of these sets.</p> <p>“You have your toolbox, and you try to get as much out of it as you can for a problem,” he says, but sometimes you have to seek out new tools. “MIT is a great place for that.”</p> <p>That act of reaching across fields is fundamental to the practice of mathematics, he says. The book that he recently published with Zworski opens with a quote from Goethe: “Mathematicians are Frenchmen of sorts: Whatever one says to them they translate into their own language and then it becomes something entirely different.”</p> <p>Dyatlov sees a connection between this epigraph and his own forays into the correspondence between math and physics.</p> <p>“It’s an ironic take on that,” he says. “There’s a natural repelling force for math and physics to diverge into separate fields, because we do things so differently. Experimental physicists have to respect the reality of situation, and have to think about what you can model in a lab. As a mathematician, you focus on things you can prove. You have to distill and translate the physical phenomena into theorems.”</p> <p>“It’s up to people in communities to create an attracting force to work together and bridge this divide.”</p> Semyon DyatlovImage: M. Scott BrauerProfile, Faculty, Mathematics, Physics, School of Science Finding the true potential of algorithms Using mathematical theory, Virginia Williams coaxes algorithms to run faster or proves they’ve hit their maximum speed. Tue, 07 Jan 2020 00:00:00 -0500 Rob Matheson | MIT News Office <p>Each semester, Associate Professor Virginia Vassilevska Williams tries to impart one fundamental lesson to her computer-science undergraduates: Math is the foundation of everything.</p> <p>Often, students come into Williams’ class, 6.006 (Introduction to Algorithms), wanting to dive into advanced programming that power the latest, greatest computing techniques. Her lessons instead focus on how algorithms are designed around core mathematical models and concepts. &nbsp;</p> <p>“When taking an algorithms class, many students expect to program a lot and perhaps use deep learning, but it’s very mathematical and has very little programming,” says Williams, the Steven G. (1968) and Renee Finn Career Development Professor who recently earned tenure in the Department of Electrical Engineering and Computer Science. “We don’t have much time together in class (only two hours a week), but I hope in that time they get to see a little of the beauty of math — because math allows you to see how and why everything works together. It really is a beautiful thing.”</p> <p>Williams’ life is very much shaped by math. As a child of two mathematician parents, she fell in love with the subject early on. But even though she excelled in the subject, her high school classes focused on German, writing, and biology. Returning to her first love in college and beyond, she applied her math skills to make waves in computer science.</p> <p>In highly influential work, Williams in 2012 improved an algorithm for “<a href="">matrix multiplication</a>” —&nbsp;a fundamental operation across computer science — that was thought to be the fastest iteration for 24 years. Years later, she co-founded an emerging field called “fine-grained complexity,” which seeks to explain, in part, how fast certain algorithms can solve various problems.</p> <p>In matrix multiplication, her work has now shifted slightly to showing that existing techniques “cannot do better,” she says. “We couldn’t improve the performance of our own algorithms anymore, so we came up with ways to explain why we couldn’t and why other methods can’t improve the performance either.”</p> <p><strong>Winding path to math</strong></p> <p>Growing up in Sofia, Bulgaria, Williams loved math and was a gifted student. But her parents often reminded her the mathematician’s life wasn’t exactly glamorous —especially when trying to find faculty gigs in the same area for two people. They sometimes traveled where work took them.</p> <p>That included a brief odyssey around the U.S. as a child. The first stop was Laramie, Wyoming. Her parents were visiting professors at the University of Wyoming, while Williams initially struggled through fourth grade because of the language barrier. “I didn’t really speak English, and was thrown into this school. My brother and I learned English watching the Disney channel, which was pretty fun,” says Williams, who today speaks Bulgarian, English, German, and some Russian.</p> <p>The next stop was Los Angeles — right around the time of the Rodney King riots. “The house on the other side of our street was set on fire,” Williams recalls. “Those were some very strange memories of L.A.”</p> <p>Returning to Bulgaria after two years, Williams decided to “explore her options” outside math by enrolling in the German Language High School in Sofia, the country’s top high school at the time, where she studied the German language, literature, history, and other humanities subjects. But, when it came to applying to colleges, she could never shake her first love. “I really tried to like the humanities, and what I learned is very helpful to me nowadays. But those subjects were very hard for me. My brain just doesn’t work that way,” she says. “I went back to what I like.”</p> <p><strong>Transfixed by algorithms</strong></p> <p>In 1999, Williams enrolled in Caltech. In her sophomore year, she became smitten by an exciting new field: computer science. “I took my first programming course, and I loved it,” she says.</p> <p>She became transfixed by matrix multiplication algorithms, which have some heavy-duty math at their core. These algorithms compute multiple arrays of numbers corresponding to some data and output a single combined matrix of some target values. Applications are wide-ranging, including computer graphics, product design, artificial intelligence, and biotechnology.</p> <p>As a PhD student at Carnegie Mellon, and beyond, she published <a href="">numerous papers</a>, on topics such as developing fast matrix multiplication algorithms in special algebraic structures, with applications including flight scheduling and network routing. After earning her PhD, she took on a series of postdoc and researcher positions at the Institute for Advanced Study, the University of California at Berkeley, and Stanford University, where she landed a faculty position in 2013 teaching courses on algorithms.</p> <p>In 2012, she developed a new algorithm that was faster than the Coppersmith–Winograd algorithm, which had reigned supreme in matrix multiplication since the 1980s. Williams’ method reduced the number of steps required to multiply matrices. Her algorithm is only slightly slower than the current record-holder.</p> <p><strong>Dealing with complexity</strong></p> <p>Between 2010 and 2015, Williams and her husband, Ryan Williams, who is also an MIT professor, became main founders of “fine-grained complexity.” The older field of “computational complexity” finds provably efficient algorithms and algorithms that are probably inefficient, based on some threshold of computational steps they take to solve a problem.</p> <p>Fine-grained complexity groups problems together by computational equivalence to better prove if algorithms are truly optimal or not. For instance, two problems may appear very different in what they solve and how many steps algorithms take to solve them. But fine-grained complexity shows such problems are secretly the same. Therefore, if an algorithm exists for one problem that uses fewer steps, then there must exist an algorithm for the other problem that uses fewer steps, and vice versa. On the flip side, if there exists a provably optimal algorithm for one problem, then all equivalent problems must have optimal algorithms. If someone ever finds a much faster algorithm for one problem, all the equivalent problems can be solved faster.</p> <p>Since co-launching the field, “it’s ballooned,” Williams says. “For most theoretical computer science conferences, you can now submit your paper under the heading ‘fine-grained complexity.’”</p> <p>In 2017, Williams came to MIT, where she says she has found impassioned, likeminded researchers. Many graduate students and colleagues, for instance, are working in topics related to fine-grained complexity. In turn, her students have introduced her to other subjects, such as cryptography, where she’s now introducing ideas from fine-grained complexity.</p> <p>She also sometimes studies “computational social choice,” a field that caught her eye during graduate school. Her work focuses on examining the computational complexity needed to rig sports games, voting schemes, and other systems where competitors are placed in paired brackets. If someone knows, for instance, which player will win in paired match-ups, a tournament organizer can place all players in specific positions in the initial seeding to ensure a certain player wins it all.</p> <p>Simulating all the possible combinations to rig these schemes can be very computationally complex. But Williams, an avid tennis player, authored a 2010 <a href="">paper</a> that found it’s fairly simple to rig a single-elimination tournament so a certain player wins, depending on accurate predictions for match-up winners and other factors.</p> <p>This year she co-wrote a <a href="">paper</a> that showed a tournament organizer could arrange an initial seeding and bribe certain top players — within a specific budget —&nbsp;to ensure a favorite player wins the tournament. “When I need a break from my usual work, I work in this field,” Williams says. “It’s a fun change of pace.”</p> <p>Thanks to the ubiquity of computing today, Williams’ graduate students often enter her classroom far more experienced in computer science than she was at their age. But to help steer them down a distinct path, she draws inspiration from her own college experiences, getting hooked on specific topics she still pursues today.</p> <p>“In order to do good research, you have to obsess over a problem,” Williams says. “I want them to find something in my course they can obsess over.”</p> Virginia WilliamsImage: Jared CharneyResearch, Computer science and technology, Algorithms, Profile, Faculty, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, Mathematics School of Science recognizes members with 2020 Infinite Kilometer Awards Four members of the School of Science honored for contributions to the Institute. Fri, 03 Jan 2020 10:30:01 -0500 School of Science <p>The MIT <a href="">School of Science</a> has announced the winners of the 2020 Infinite Kilometer Awards, which are presented annually to researchers within the school who are exceptional contributors to their communities.</p> <p>These winners are nominated by their peers and mentors for their hard work, which can include mentoring and advising, supporting educational programs, providing service to groups such as the MIT Postdoctoral Association, or some other form of contribution to their home departments, labs, and research centers, the school, and the Institute.</p> <p>The 2020 Infinite Kilometer Award winners in the School of Science are:</p> <ul> <li><a href="" target="_blank">Edgar Costa</a>, a research scientist in the Department of Mathematics, nominated by Professor Bjorn Poonen and Principal Research Scientist Andrew Sutherland;</li> <li><a href="" target="_blank">Casey Rodriguez</a>, an instructor in the Department of Mathematics, nominated by Professor Gigliola Staffilani;</li> <li><a href="" target="_blank">Rachel Ryskin</a>, a postdoc in the Department of Brain and Cognitive Sciences, nominated by Professor Edward Gibson; and</li> <li><a href="" target="_blank">Grayson Sipe</a>, a postdoc in the Picower Institute for Learning and Memory, nominated by Professor Mriganka Sur.</li> </ul> <p>A monetary award is granted to recipients, and a celebratory reception will be held later this spring in their honor, attended by those who nominated them, family, and friends, in addition to the soon-to-be-announced recipients of the 2020 Infinite Mile Award.</p> School of Science, Mathematics, Brain and cognitive sciences, Picower Institute, Awards, honors and fellowships, Graduate, postdoctoral, Staff, Community How strong is your knot? With help from spaghetti and color-changing fibers, a new mathematical model predicts a knot’s stability. Thu, 02 Jan 2020 14:00:00 -0500 Jennifer Chu | MIT News Office <p>In sailing, rock climbing, construction, and any activity requiring the securing of ropes, certain knots are known to be stronger than others. Any seasoned sailor knows, for instance, that one type of knot will secure a sheet to a headsail, while another is better for hitching a boat to a piling.&nbsp;</p> <p>But what exactly makes one knot more stable than another has not been well-understood, until now.&nbsp;</p> <p>MIT mathematicians and engineers have developed a mathematical model that predicts how stable a knot is, based on several key properties, including the number of crossings involved and the direction in which the rope segments twist as the knot is pulled tight.&nbsp;</p> <p>“These subtle differences between knots critically determine whether a knot is strong or not,” says Jörn Dunkel, associate professor of mathematics at MIT. “With this model, you should be able to look at two knots that are almost identical, and be able to say which is the better one.”</p> <p>“Empirical knowledge refined over centuries has crystallized out what the best knots are,” adds Mathias Kolle, the Rockwell International Career Development Associate Professor at MIT. “And now the model shows why.”</p> <p>Dunkel, Kolle, and PhD students Vishal Patil and Joseph Sandt have published their results today in the journal Science.&nbsp;</p> <p><strong>Pressure’s color</strong></p> <p>In 2018, Kolle’s group engineered stretchable fibers that change color in response to strain or pressure. The researchers showed that when they pulled on a fiber, its hue changed from one color of the rainbow to another, particularly in areas that experienced the greatest stress or pressure.&nbsp;</p> <p><img alt="" src="/sites/" style="width: 500px; height: 281px;" /></p> <p><em><span style="font-size:10px;">An example of overhand knots.</span></em></p> <p>Kolle, an associate professor of mechanical engineering, was invited by MIT’s math department to give a talk on the fibers. Dunkel was in the audience and began to cook up an idea: What if the pressure-sensing fibers could be used to study the stability in knots?&nbsp;</p> <p>Mathematicians have long been intrigued by knots, so much so that physical knots have inspired an entire subfield of topology known as knot theory — the study of theoretical knots whose ends, unlike actual knots, are joined to form a continuous pattern. In knot theory, mathematicians seek to describe a knot in mathematical terms, along with all the ways that it can be twisted or deformed while still retaining its topology, or general geometry.&nbsp;</p> <p>“In mathematical knot theory, you throw everything out that’s related to mechanics,” Dunkel says. “You don’t care about whether you have a stiff versus soft fiber — it’s the same knot from a mathematician’s point of view. But we wanted to see if we could add something to the mathematical modeling of knots that accounts for their mechanical properties, to be able to say why one knot is stronger than another.”&nbsp;</p> <p><strong>Spaghetti physics</strong></p> <p>Dunkel and Kolle teamed up to identify what determines a knot’s stability. The team first used Kolle’s fibers to tie a variety of knots, including the trefoil and figure-eight knots — configurations that were familiar to Kolle, who is an avid sailor, and to rock-climbing members of Dunkel’s group. They photographed each fiber, noting where and when the fiber changed color, along with the force that was applied to the fiber as it was pulled tight.</p> <p>The researchers used the data from these experiments to calibrate a model that Dunkel’s group previously implemented to describe another type of fiber: spaghetti. In that model, Patil and Dunkel described the behavior of spaghetti and other flexible, rope-like structures by treating each strand as a chain of small, discrete, spring-connected beads. The way each spring bends and deforms can be calculated based on the force that is applied to each individual spring.&nbsp;</p> <p>Kolle’s student Joseph Sandt had previously drawn up a color map based on experiments with the fibers, which correlates a fiber’s color with a given pressure applied to that fiber. Patil and Dunkel incorporated this color map into their spaghetti model, then used the model to simulate the same knots that the researchers had tied physically using the fibers. When they compared the knots in the experiments with those in the simulations, they found the pattern of colors in both were virtually the same — a sign that the model was accurately simulating the distribution of stress in knots.&nbsp;</p> <p>With confidence in their model, Patil then simulated more complicated knots, taking note of which knots experienced more pressure and were therefore stronger than other knots. Once they categorized knots based on their relative strength, Patil and Dunkel looked for an explanation for why certain knots were stronger than others. To do this, they drew up simple diagrams for the well-known granny, reef, thief, and grief knots, along with more complicated ones, such as the carrick, zeppelin, and Alpine butterfly.</p> <p><img alt="" src="/sites/" style="width: 500px; height: 281px;" /></p> <p><em><span style="font-size:10px;"><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); font-family: Calibri, sans-serif;">An example of a reef knot.</span></span></em></p> <p>Each knot diagram depicts the pattern of the two strands in a knot before it is pulled tight. The researchers included the direction of each segment of a strand as it is pulled, along with where strands cross. They also noted the direction each segment of a strand rotates as a knot is tightened.&nbsp;</p> <p>In comparing the diagrams of knots of various strengths, the researchers were able to identify general “counting rules,” or characteristics that determine a knot’s stability. Basically, a knot is stronger if it has more strand crossings, as well as more “twist fluctuations” — changes in the direction of rotation from one strand segment to another.&nbsp;</p> <p>For instance, if a fiber segment is rotated to the left at one crossing and rotated to the right at a neighboring crossing as a knot is pulled tight, this creates a twist fluctuation and thus opposing friction, which adds stability to a knot. If, however, the segment is rotated in the same direction at two neighboring crossing, there is no twist fluctuation, and the strand is more likely to rotate and slip, producing a weaker knot.&nbsp;</p> <p>They also found that a knot can be made stronger if it has more “circulations,” which they define as a region in a knot where two parallel strands loop against each other in opposite directions, like a circular flow.&nbsp;</p> <p>By taking into account these simple counting rules, the team was able to explain why a reef knot, for instance, is stronger than a granny knot. While the two are almost identical, the reef knot has a higher number of twist fluctuations, making it a more stable configuration. Likewise, the zeppelin knot, because of its slightly higher circulations and twist fluctuations, is stronger, though possibly harder to untie, than the Alpine butterfly — a knot that is commonly used in climbing.&nbsp;</p> <p>“If you take a family of similar knots from which empirical knowledge singles one out as “the best,” now we can say why it might deserve this distinction,” says Kolle, who envisions the new model can be used to configure knots of various strengths to suit particular applications. “We can play knots against each other for uses in suturing, sailing, climbing, and construction. It’s wonderful.”</p> <p>This research was supported, in par,t by the Alfred P. Sloan Foundation, the James S. McDonnell Foundation, the Gillian Reny Stepping Strong Center for Trauma Innovation at Brigham and Women’s Hospital, and the National Science Foundation</p> With the help of color-changing fibers, MIT researchers develop a mathematical model to predict a knot’s stability.Image courtesy of the researchers Computer modeling, Mathematics, Mechanical engineering, Research, School of Engineering, School of Science, National Science Foundation (NSF) A new way to remove contaminants from nuclear wastewater Method concentrates radionuclides in a small portion of a nuclear plant’s wastewater, allowing the rest to be recycled. Thu, 19 Dec 2019 09:23:05 -0500 David L. Chandler | MIT News Office <p>Nuclear power continues to expand globally, propelled, in part, by the fact that it produces few greenhouse gas emissions while providing steady power output. But along with that expansion comes an increased need for dealing with the large volumes of water used for cooling these plants, which becomes contaminated with radioactive isotopes that require special long-term disposal.</p> <p>Now, a method developed at MIT provides a way of substantially reducing the volume of contaminated water that needs to be disposed of, instead concentrating the contaminants and allowing the rest of the water to be recycled through the plant’s cooling system. The proposed system is described in the journal <em>Environmental Science and Technology</em>, in a paper by graduate student Mohammad Alkhadra, professor of chemical engineering Martin Bazant, and three others.</p> <p>The method makes use of a process called shock electrodialysis, which uses an electric field to generate a deionization shockwave in the water. The shockwave pushes the electrically charged particles, or ions, to one side of a tube filled with charged porous material, so that concentrated stream of contaminants can be separated out from the rest of the water. The group discovered that two radionuclide contaminants — isotopes of cobalt and cesium — can be selectively removed from water that also contains boric acid and lithium. After the water stream is cleansed of its cobalt and cesium contaminants, it can be reused in the reactor.</p> <p>The shock electrodialysis process was initially developed by Bazant and his co-workers as a general method of removing salt from water, as demonstrated in their <a href="">first scalable prototype</a> four years ago. Now, the team has focused on this more specific application, which could help improve the economics and environmental impact of working nuclear power plants. In ongoing research, they are also continuing to develop a system for removing other contaminants, including lead, from drinking water.</p> <p>Not only is the new system inexpensive and scalable to large sizes, but in principle it also can deal with a wide range of contaminants, Bazant says. “It’s a single device that can perform a whole range of separations for any specific application,” he says.</p> <p>In their earlier desalination work, the researchers used measurements of the water’s electrical conductivity to determine how much salt was removed. In the years since then, the team has developed other methods for detecting and quantifying the details of what’s in the concentrated radioactive waste and the cleaned water.</p> <p>“We carefully measure the composition of all the stuff going in and out,” says Bazant, who is the E.G. Roos Professor of Chemical Engineering as well as a professor of mathematics. “This really opened up a new direction for our research.” They began to focus on separation processes that would be useful for health reasons or that would result in concentrating material that has high value, either for reuse or to offset disposal costs.</p> <p>The method they developed works for sea water desalination, but it is a relatively energy-intensive process for that application. The energy cost is dramatically lower when the method is used for ion-selective separations from dilute streams such as nuclear plant cooling water. For this application, which also requires expensive disposal, the method makes economic sense, he says. It also hits both of the team’s targets: dealing with high-value materials and helping to safeguard health. The scale of the application is also significant — a single large nuclear plant can circulate about 10 million cubic meters of water per year through its cooling system, Alkhadra says.</p> <p>For their tests of the system, the researchers used simulated nuclear wastewater based on a recipe provided by Mitsubishi Heavy Industries, which sponsored the research and is a major builder of nuclear plants. In the team’s tests, after a three-stage separation process, they were able to remove 99.5 percent of the cobalt radionuclides in the water while retaining about 43 percent of the water in cleaned-up form so that it could be reused. As much as two-thirds of the water can be reused if the cleanup level is cut back to 98.3 percent of the contaminants removed, the team found.</p> <p>While the overall method has many potential applications, the nuclear wastewater separation, is “one of the first problems we think we can solve [with this method] that no other solution exists for,” Bazant says. No other practical, continuous, economic method has been found for separating out the radioactive isotopes of cobalt and cesium, the two major contaminants of nuclear wastewater, he adds.</p> <p>While the method could be used for routine cleanup, it could also make a big difference in dealing with more extreme cases, such as the millions of gallons of contaminated water at the damaged Fukushima Daichi power plant in Japan, where the accumulation of that contaminated water has threatened to overpower the containment systems designed to prevent it from leaking out into the adjacent Pacific. While the new system has so far only been tested at much smaller scales, Bazant says that such large-scale decontamination systems based on this method might be possible “within a few years.”</p> <p>The research team also included MIT postdocs Kameron Conforti and Tao Gao and graduate student Huanhuan Tian.</p> A small-scale device, seen here, was used in the lab to demonstrate the effectiveness of the new shockwave-based system for removing radioactive contaminants from the cooling water in nuclear powerplants.Image courtesy of the researchers Research, School of Engineering, Chemical engineering, Energy, Water, Desalination, Mathematics, Nuclear science and engineering Researchers generate terahertz laser with laughing gas Device may enable “T-ray vision” and better wireless communication. Thu, 14 Nov 2019 13:59:59 -0500 Jennifer Chu | MIT News Office <p>Within the electromagnetic middle ground between microwaves and visible light lies terahertz radiation, and the promise of “T-ray vision.”</p> <p>Terahertz waves have frequencies higher than microwaves and lower than infrared and visible light. Where optical light is blocked by most materials, terahertz waves can pass straight through, similar to microwaves. If they were fashioned into lasers, terahertz waves might enable “T-ray vision,” with the ability to see through clothing, <a href="">book covers</a>, and other thin materials. Such technology could produce crisp, higher-resolution images than microwaves, and be far safer than X-rays.</p> <p>The reason we don’t see T-ray machines in, for instance, airport security lines and medical imaging facilities is that producing terahertz radiation requires very large, bulky setups or devices, many operating at ultracold temperatures, that produce terahertz radiation at a single frequency — not very useful, given that a wide range of frequencies is required to penetrate various materials.</p> <p>Now researchers from MIT, Harvard University, and the U.S. Army have built a compact device, the size of a shoebox, that works at room temperature to produce a terahertz laser whose frequency they can tune over a wide range. The device is built from commercial, off-the-shelf parts and is designed to generate terahertz waves by spinning up the energy of molecules in nitrous oxide, or, as it’s more commonly known, laughing gas.</p> <p>Steven Johnson, professor of mathematics at MIT, says that in addition to T-ray vision, terahertz waves can be used as a form of wireless communication, carrying information at a higher bandwidth than radar, for instance, and doing so across distances that scientists can now tune using the group’s device.</p> <p>“By tuning the terahertz frequency, you can choose how far the waves can travel through air before they are absorbed, from meters to kilometers, which gives precise control over who can ‘hear’ your terahertz communications or ‘see’ your terahertz radar,” Johnson says.&nbsp;“Much like changing the dial on your radio, the ability to easily tune a terahertz source is crucial to opening up new applications in wireless communications, radar, and spectroscopy.”</p> <p>Johnson and his colleagues have published their results today in the journal <em>Science</em>. Co-authors include MIT postdoc Fan Wang, along with Paul Chevalier, Arman Amirzhan, Marco Piccardo, and Federico Capasso of Harvard University, and Henry Everitt of the U.S. Army Combat Capabilities Development Command Aviation and Missile Center.</p> <p><strong>Molecular breathing room</strong></p> <p>Since the 1970s, scientists have experimented with generating terahertz waves using molecular gas lasers — setups in which a high-powered infrared laser is shot into a large tube filled with gas (typically methyl fluoride) whose molecules react by vibrating and eventually rotating. The rotating molecules can jump from one energy level to the next, the difference of which is emitted as a sort of leftover energy, in the form of a photon in the terahertz range. As more photons build up in the cavity, they produce a terahertz laser.</p> <p>Improving the design of these gas lasers has been hampered by unreliable theoretical models, the researchers say. In small cavities at high gas pressures, the models predicted that, beyond a certain pressure, the molecules would be too “cramped” to spin and emit terahertz waves. Partly for this reason, terahertz gas lasers typically used meters-long cavities and large infrared lasers.&nbsp;&nbsp;</p> <p>However, in the 1980s, Everitt found that he was able to produce terahertz waves in his laboratory using a gas laser that was much smaller than traditional devices, at pressures far higher than the models said was possible. This discrepancy was never fully explained, and work on terahertz gas lasers fell by the wayside in favor of other approaches.</p> <p>A few years ago, Everitt mentioned this theoretical mystery to Johnson when the two were collaborating on other work as part of MIT’s Institute for Soldier Nanotechnologies. Together with Everitt, Johnson and Wang took up the challenge, and ultimately formulated a new mathematical theory to describe the behavior of a gas in a molecular gas laser cavity. The theory also successfully explained how terahertz waves could be emitted, even from very small, high-pressure cavities.</p> <p>Johnson says that while gas molecules can vibrate at multiple frequencies and rotational rates in response to an infrared pump, previous theories discounted many of these vibrational states and assumed instead that a handful of vibrations were what ultimately mattered in producing a terahertz wave. If a cavity were too small, previous theories suggested that molecules vibrating in response to an incoming infrared laser would collide more often with each other, releasing their energy rather than building it up further to spin and produce terahertz.</p> <p>Instead, the new model tracked thousands of relevant vibrational and rotational states among millions of groups of molecules within a single cavity, using new computational tricks to make such a large problem tractable on a laptop computer. It then analyzed how those molecules would react to incoming infrared light, depending on their position and direction within the cavity.</p> <p>“We found that when you include all these other vibrational states that people had been throwing out, they give you a buffer,” Johnson says. “In simpler models, the molecules are rotating, but when they bang into other molecules they lose everything. Once you include all these other states, that doesn’t happen anymore. These collisions can transfer energy to other vibrational states, and sort of give you more breathing room to keep rotating and keep making terahertz waves.”</p> <p><strong>Laughing, dialed up</strong></p> <p>Once the team found that their new model accurately predicted what Everitt observed decades ago, they collaborated with Capasso’s group at Harvard to design a new type of compact terahertz generator by combining the model with new gases and a new type of infrared laser.</p> <p>For the infrared source, the researchers used a quantum cascade laser, or QCL — a more recent type of laser that is compact and also tunable.</p> <p>“You can turn a dial, and it changes the frequency of the input laser, and the hope was that we could use that to change the frequency of the terahertz coming out,” Johnson says.</p> <p>The researchers teamed up with Capasso, a pioneer in the development of QCLs, who provided a laser that produced a range of power that their theory predicted would work with a cavity the size of a pen (about 1/1,000 the size of a conventional cavity). The researchers then looked for a gas to spin up.</p> <p>The team searched through libraries of gases to identify those that were known to rotate in a certain way in response to infrared light, eventually landing on nitrous oxide, or laughing gas, as an ideal and accessible candidate for their experiment.</p> <p>They ordered laboratory-grade nitrous oxide, which they pumped into a pen-sized cavity. When they sent infrared light from the QCL into the cavity, they found they could produce a terahertz laser. As they tuned the QCL, the frequency of terahertz waves also shifted, across a wide range.</p> <p>“These demonstrations confirm the universal concept of a terahertz molecular laser source which can be broadly tunable across its entire rotational states when pumped by a continuously tunable QCL,” Wang says.</p> <p>Since these initial experiments, the researchers have extended their mathematical model to include a variety of other gas molecules, such as carbon monoxide and ammonia, providing scientists with a menu of different terahertz generation options with different frequencies and tuning ranges, paired with a QCL matched to each gas. The group’s theoretical tools also enable scientists to tailor the cavity design to different applications. They are now pushing toward more focused beams and higher powers, with commercial development on the horizon.</p> <p>Johnson says scientists can refer to the group’s mathematical model to design new, compact and tunable terahertz lasers, using other gases and experimental parameters.</p> <p>“These gas lasers were for a long time seen as old technology, and people assumed these were huge, low-power, nontunable things, so they looked to other terahertz sources,” Johnson says. “Now we’re saying they can be small, tunable, and much more efficient. You could fit this in your backpack, or in your vehicle for wireless communication or high-resolution imaging. Because you don’t want a cyclotron in your car.”</p> <p>This research was supported in part by the U.S. Army Research Office and the National Science Foundation.</p> A new shoebox-sized laser produces terahertz waves (green squiggles) by using a special infrared laser (red) to rotate molecules of nitrous oxide, or laughing gas, packed in a pen-sized cavity (grey).Courtesy of Chad Scales, US Army Futures CommandEnergy, Mathematics, Physics, Research, School of Science, Wireless, National Science Foundation (NSF), Department of Defense (DOD) Student gets a kick out of French internship Math and computer science major tweaks soccer accelerometer’s algorithms and expands his worldview via MIT-France. Mon, 04 Nov 2019 12:55:01 -0500 Sandi Miller | Department of Mathematics <p>Like many European countries, France takes its soccer — or football — very seriously. To further refine the game of top players, a French startup called <a href="" target="_blank">Footbar</a> created a smart device to measure players’ stats such as speed, stamina, and skill. The artificial intelligence used by the Meteor tracker&nbsp;determines the quality of passes, shots, dribbles, and tackles based only on the acceleration of the tracker worn on the player’s calf.</p> <p>However, the AI was unable to tell whether some athletes “cheated” by wearing the tracker on their ankles, which provided better statistics than when wearing it on their calves. To solve the problem, they sought out an intern at MIT.&nbsp;&nbsp;</p> <p>When math and computer science junior&nbsp;Benton&nbsp;Wilson applied and was accepted for an internship through&nbsp;<a href="">MIT-France</a>, part of the MIT International Science and Technology Initiatives (MISTI), he had only a few expectations: to do something data science-related, to practice his high school French, and “to gain some perspective for my worldview.”&nbsp;</p> <p>He joined four French interns at&nbsp;Footbar, located within Paris’ Le Tremplin, an incubator for sports-related companies. At first, he was "un peu nerveux" about using his French skills in a professional setting, but his high school soccer skills came in handy when he tried out the quarter-sized device himself.</p> <p>His first task was to create&nbsp;algorithms that would determine where a player was wearing the tracker.</p> <p>Players can upload their stats to their computer, view their progress, and compare their results online against teammates, as well as Meteor-wearing clients all over the world. “At the end of a game, you can get your stats for the game, and then you can also make a profile and see how you change over time,” explains Wilson.</p> <p>But results are skewed depending on where players wear the tracker. Footbar decided that Wilson needed to develop an algorithm that would “penalize” the ankle-wearing players.</p> <p>“It is a pretty complicated task for someone discovering the data,” says Wilson’s supervisor at Footbar, data scientist Sébastien Benoit. “His first weeks were probably a bit complicated for him as he both had to get more familiar with both our stack (understand how our code works) and with the type of data we work with (time-series data from an accelerator).”</p> <p>Over the summer, Wilson&nbsp;used his background in machine learning and Python, and picked up skills in GitHub, the&nbsp;database system Django,&nbsp;and signal processing, to work on the algorithm. Not only did Wilson solve the problem, he discovered some technical solutions that further impressed his supervisor.</p> <p>“After showing him a few examples,&nbsp;Benton&nbsp;was able to build a model and train it on enough data to make it work well,” says Benoit. He says that Wilson’s model was 95 percent accurate, and is now being used by Footbar’s production department. “We can now successfully detect the smart guys who intentionally exploited this flaw. Thank you,&nbsp;Benton!”</p> <p>Benoit was impressed enough to let Wilson work independently, which led to him spending his final two weeks of his internship solving a second problem that had vexed Footbar: to&nbsp;automatically detect which of four fitness tests was taken by an athlete: sprint; sprint down and back; running endurance test; and vertical jump test. “Some of it was difficult, such as detecting when jumps tests occur versus other kicks/jumps, but overall I just worked on trying different things,” Wilson says.&nbsp; &nbsp;&nbsp;</p> <p>“This task [was] probably twice as difficult as the previous one, but&nbsp;Benton&nbsp;completed this quite well and pretty quickly,” Benoit says.&nbsp;</p> <p>Wilson added that he appreciated working within a tight-knit community that began each day with standup meetings, ate lunch together, and gathered for soccer, cross-fit training, and jogs.</p> <p>In his off-hours, Wilson shared an apartment with a fellow MIT-France intern who was researching environmental sustainability for another company. Wilson joined a local gym, shopped in local markets, watched old movies at the Latin Quarter’s Le Champo theater, attended the&nbsp;China vs. South Africa Women’s World Cup match, ventured to different parts of France, traveled to Barcelona and the Netherlands,&nbsp;and people-watched along the Seine River.&nbsp;</p> <p>“My favorite areas were over near the Canal de St. Martin and La Villette, where there are a ton of restaurants and places to sit along the canals,” he recalls.</p> <p>He&nbsp;was one of 45 MIT students who&nbsp;participated in MISTI’s&nbsp;<a href="">MIT-France</a>&nbsp;this past summer. Its internship program,&nbsp;founded in 2001 with a collaboration between the French Ministry of Foreign Affairs and MIT,&nbsp;provides opportunities for research and experience in French companies and labs.</p> <p>While the logistics of studying abroad can be daunting for many students, Wilson and his peers received a lot of help. MISTI programs cover basic needs, including airfare, housing, and food; Wilson and several others were also co-sponsored by the MIT&nbsp;<a href="">European Club</a>. Additionally, students receive support with their visas and other paperwork. MIT-France provides a comprehensive 377-page student guidebook to living in France. Student internships are between three to six months; Wilson stayed for three.</p> <p>“MISTI has provided me with a unique opportunity to immerse myself in a totally new environment,” says Wilson,&nbsp;who adds that his experience gave him the confidence to consider&nbsp;an international career after graduation.</p> <p>The deadline for next summer's internships is Dec. 1 for priority applicants.&nbsp;To apply, please visit<em> </em><a href=""></a>.&nbsp;</p> MIT student Benton WilsonPhoto: Benton WilsonMathematics, MISTI, France, School of Science, Students, Undergraduate, Europe, Computer science and technology, International initiatives School of Science appoints 14 faculty members to named professorships Those selected for these positions receive additional support to pursue their research and develop their careers. Mon, 04 Nov 2019 11:50:01 -0500 School of Science <p>The <a href="">School of Science</a> has announced that 14 of its faculty members have been appointed to named professorships. The faculty members selected for these positions receive additional support to pursue their research and develop their careers.</p> <p><a href="">Riccardo Comin</a> is an assistant professor in the Department of Physics. He has been named a Class of 1947 Career Development Professor. This three-year professorship is granted in recognition of the recipient's outstanding work in both research and teaching. Comin is interested in condensed matter physics. He uses experimental methods to synthesize new materials, as well as analysis through spectroscopy and scattering to investigate solid state physics. Specifically, the Comin lab attempts to discover and characterize electronic phases of quantum materials. Recently, his lab, in collaboration with colleagues, discovered that weaving a conductive material into a particular pattern known as the “kagome” pattern can result in quantum behavior when electricity is passed through.</p> <p><a href="">Joseph Davis</a>, assistant professor in the Department of Biology, has been named a Whitehead Career Development Professor. He looks at how cells build and deconstruct complex molecular machinery. The work of his lab group relies on biochemistry, biophysics, and structural approaches that include spectrometry and microscopy. A current project investigates the formation of the ribosome, an essential component in all cells. His work has implications for metabolic engineering, drug delivery, and materials science.</p> <p><a href="">Lawrence Guth</a> is now the Claude E. Shannon (1940) Professor of Mathematics. Guth explores harmonic analysis and combinatorics, and he is also interested in metric geometry and identifying connections between geometric inequalities and topology. The subject of metric geometry revolves around being able to estimate measurements, including length, area, volume and distance, and combinatorial geometry is essentially the estimation of the intersection of patterns in simple shapes, including lines and circles.</p> <p><a href="">Michael Halassa</a>, an assistant professor in the Department of Brain and Cognitive Sciences, will hold the three-year Class of 1958 Career Development Professorship. His area of interest is brain circuitry. By investigating the networks and connections in the brain, he hopes to understand how they operate — and identify any ways in which they might deviate from normal operations, causing neurological and psychiatric disorders. Several publications from his lab discuss improvements in the treatment of the deleterious symptoms of autism spectrum disorder and schizophrenia, and his latest news provides insights on how the brain filters out distractions, particularly noise. Halassa is an associate investigator at the McGovern Institute for Brain Research and an affiliate member of the Picower Institute for Learning and Memory.</p> <p><a href="">Sebastian Lourido</a>, an assistant professor and the new Latham Family Career Development Professor in the Department of Biology for the next three years, works on treatments for infectious disease by learning about parasitic vulnerabilities. Focusing on human pathogens, Lourido and his lab are interested in what allows parasites to be so widespread and deadly, looking on a molecular level. This includes exploring how calcium regulates eukaryotic cells, which, in turn, affect processes such as muscle contraction and membrane repair, in addition to kinase responses.</p> <p><a href="">Brent Minchew</a> is named a Cecil and Ida Green Career Development Professor for a three-year term. Minchew, a faculty member in the Department of Earth, Atmospheric and Planetary Sciences, studies glaciers using modeling and remote sensing methods, such as interferometric synthetic aperture radar. His research into glaciers, including their mechanics, rheology, and interactions with their surrounding environment, extends as far as observing their responses to climate change. His group recently determined that Antarctica, in a worst-case scenario climate projection, would not contribute as much as predicted to rising sea level.</p> <p><a href="">Elly Nedivi</a>, a professor in the departments of Brain and Cognitive Sciences and Biology, has been named the <a href="">inaugural</a> William R. (1964) And Linda R. Young Professor. She works on brain plasticity, defined as the brain’s ability to adapt with experience, by identifying genes that play a role in plasticity and their neuronal and synaptic functions. In one of her lab’s recent publications, they suggest that variants of a particular gene may undermine expression or production of a protein, increasing the risk of bipolar disorder. In addition, she collaborates with others at MIT to develop new microscopy tools that allow better analysis of brain connectivity. Nedivi is also a member of the Picower Institute for Learning and Memory.</p> <p><a href="">Andrei Negu</a><a href="" target="_blank">ț</a> has been named a Class of 1947 Career Development Professor for a three-year term. Neguț, a member of the Department of Mathematics, fixates on problems in geometric representation theory. This topic requires investigation within algebraic geometry and representation theory simultaneously, with implications for mathematical physics, symplectic geometry, combinatorics and probability theory.</p> <p><a href="">Matĕj Peč</a>, the Victor P. Starr Career Development Professor in the Department of Earth, Atmospheric and Planetary Science until 2021, studies how the movement of the Earth’s tectonic plates affects rocks, mechanically and microstructurally. To investigate such a large-scale topic, he utilizes high-pressure, high-temperature experiments in a lab to simulate the driving forces associated with plate motion, and compares results with natural observations and theoretical modeling. His lab has identified a particular boundary beneath the Earth’s crust where rock properties shift from brittle, like peanut brittle, to viscous, like honey, and determined how that layer accommodates building strain between the two. In his investigations, he also considers the effect on melt generation miles underground.</p> <p><a href="">Kerstin Perez</a> has been named the three-year Class of 1948 Career Development Professor in the Department of Physics. Her research interest is dark matter. She uses novel analytical tools, such as those affixed on a balloon-borne instrument that can carry out processes similar to that of a particle collider (like the Large Hadron Collider) to detect new particle interactions in space with the help of cosmic rays. In another research project, Perez uses a satellite telescope array on Earth to search for X-ray signatures of mysterious particles. Her work requires heavy involvement with collaborative observatories, instruments, and telescopes. Perez is affiliated with the Kavli Institute for Astrophysics and Space Research.</p> <p><a href="">Bjorn Poonen</a>, named a Distinguished Professor of Science in the Department of Mathematics, studies number theory and algebraic geometry. He and his colleagues generate algorithms that can solve polynomial equations with the particular requirement that the solutions be rational numbers. These types of problems can be useful in encoding data. He also helps to determine what is undeterminable, that is exploring the limits of computing.</p> <p><a href="">Daniel Suess</a>, named a Class of 1948 Career Development Professor in the Department of Chemistry, uses molecular chemistry to explain global biogeochemical cycles. In the fields of inorganic and biological chemistry, Suess and his lab look into understanding complex and challenging reactions and clustering of particular chemical elements and their catalysts. Most notably, these reactions include those that are essential to solar fuels. Suess’s efforts to investigate both biological and synthetic systems have broad aims of both improving human health and decreasing environmental impacts.</p> <p><a href="">Alison Wendlandt</a> is the new holder of the five-year Cecil and Ida Green Career Development Professorship. In the Department of Chemistry, the Wendlandt research group focuses on physical organic chemistry and organic and organometallic synthesis to develop reaction catalysts. Her team fixates on designing new catalysts, identifying processes to which these catalysts can be applied, and determining principles that can expand preexisting reactions. Her team’s efforts delve into the fields of synthetic organic chemistry, reaction kinetics, and mechanics.</p> <p><a href="">Julien de Wit</a>, a Department of Earth, Atmospheric and Planetary Sciences assistant professor, has been named a Class of 1954 Career Development Professor. He combines math and science to answer questions about big-picture planetary questions. Using data science, de Wit develops new analytical techniques for mapping exoplanetary atmospheres, studies planet-star interactions of planetary systems, and determines atmospheric and planetary properties of exoplanets from spectroscopic information. He is a member of the scientific team involved in the Search for habitable Planets EClipsing ULtra-cOOl Stars (SPECULOOS) and the TRANsiting Planets and Planetesimals Small Telescope (TRAPPIST), made up of an international collection of observatories. He is affiliated with the Kavli Institute.</p> Clockwise from top left: Riccardo Comin, Joseph Davis, Lawrence Guth, Michael Halassa, Sebastian Lourido, Brent Minchew, Elly Nedivi, Andrei Neguț, Matĕj Peč, Kerstin Perez, Bjorn Poonen, Daniel Suess, Alison Wendlandt, and Julien de Wit Photos courtesy of the faculty.School of Science, Physics, Biology, Mathematics, Brain and cognitive sciences, McGovern Institute, Picower Institute, EAPS, Kavli Institute, Chemistry, Faculty, Awards, honors and fellowships Nature can help solve optimization problems A low-cost analog circuit based on synchronizing oscillators could scale up quickly and cheaply to beat out digital computers. Mon, 28 Oct 2019 11:15:01 -0400 Kylie Foy | Lincoln Laboratory <p>Today's best digital computers still struggle to solve, in a practical time frame, a certain class of problem: combinatorial optimization problems, or those that involve combing through large sets of possibilities to find the best solution. Quantum computers hold potential to take on these problems, but scaling up the number of quantum bits in these systems remains a hurdle.&nbsp;</p> <p>Now, MIT Lincoln Laboratory researchers have demonstrated an alternative, analog-based way to accelerate the computing of these problems. "Our computer works by 'computing with physics' and uses nature itself to help solve these tough optimization problems," says Jeffrey Chou, co-lead author of a paper about <a href="" target="_blank">this work</a> published in Nature's <em>Scientific Reports</em>. "It's made of standard electronic components, allowing us to scale our computer quickly and cheaply by leveraging the existing microchip industry."</p> <div class="cms-placeholder-content-video"></div> <p>Perhaps the most well-known combinatorial optimization problem is that of the traveling salesperson. The problem asks to find the shortest route a salesperson can take through a number of cities, starting and ending at the same one. It may seem simple with only a few cities, but the problem becomes exponentially difficult to solve as the number of cities grows, bogging down even the best supercomputers. Yet optimization problems need to be solved in the real world daily; the solutions are used to schedule shifts, minimize financial risk, discover drugs, plan shipments, reduce interference on wireless networks, and much more.</p> <p>"It has been known for a very long time that digital computers are fundamentally bad at solving these types of problems," says Suraj Bramhavar, also a co-lead author. "Many of the algorithms that have been devised to find solutions have to trade off solution quality for time. Finding the absolute optimum solution winds up taking an unreasonably long time when the problem sizes grow." Finding better solutions and doing so in dramatically less time could save industries billions of dollars. Thus, researchers have been searching for new ways to build systems designed specifically for optimization.</p> <p><strong>Finding the beat </strong>&nbsp;</p> <p>Nature likes to optimize energy, or achieve goals in the most efficient and distributed manner. This principle can be witnessed in the synchrony of nature, like heart cells beating together or schools of fish moving as one. Similarly, if you set two pendulum clocks on the same surface, no matter when the individual pendula are set into motion, they will eventually be lulled into a synchronized rhythm, reaching their apex at the same time but moving in opposite directions (or out of phase). This phenomenon was first observed in 1665 by the Dutch scientist Christiaan Huygens. These clocks are an example of coupled oscillators, set up in such a way that energy can be transferred between them.&nbsp;</p> <p>"We've essentially built an electronic, programmable version of this [clock setup] using coupled nonlinear oscillators," Chou says, showing a <a href="">YouTube video</a> of metronomes displaying a similar phenomenon. "The idea is that if you set up a system that encodes your problem's energy landscape, then the system will naturally try to minimize the energy by synchronizing, and in doing so, will settle on the best solution. We can then read out this solution."</p> <p>The laboratory's prototype is a type of Ising machine, a computer based on a model in physics that describes a network of magnets, each of which have a magnetic "spin" orientation that can point only up or down. Each spin's final orientation depends on its interaction with every other spin. The individual spin-to-spin interactions are defined with a specific coupling weight, which denotes the strength of their connection. The goal of an Ising machine is to find, given a specific coupling strength network, the correct configuration of each spin, up or down, that minimizes the overall system energy.</p> <p>But how does an Ising machine solve an optimization problem? It turns out that optimization problems can be mapped directly onto the Ising model, so that a set of a spins with certain coupling weights can represent each city and the distances between them in the traveling salesperson problem. Thus, finding the lowest-energy configuration of spins in the Ising model translates directly into the solution for the seller's fastest route. However, solving this problem by individually checking each of the possible configurations becomes prohibitively difficult when the problems grow to even modest sizes.&nbsp;</p> <p>In recent years, there have been efforts to build quantum machines that map to the Ising model, the most notable of which is one from the Canadian company D-Wave Systems. These machines may offer an efficient way to search the large solution space and find the correct answer, although they operate at cryogenic temperatures.</p> <p>The laboratory's system runs a similar search, but does so using simple electronic oscillators. Each oscillator represents a spin in the Ising model, and similarly takes on a binarized phase, where oscillators that are synchronized, or in phase, represent the "spin up" configuration and those that are out of phase represent the "spin down" configuration. To set the system up to solve an optimization problem, the problem is first mapped to the Ising model, translating it into programmable coupling weights connecting each oscillator.</p> <p>With the coupling weights programmed, the oscillators are allowed to run, like the pendulum arm of each clock being released. The system then naturally relaxes to its overall minimum energy state. Electronically reading out each oscillator's final phase, representing "spin up" or "spin down," presents the answer to the posed question. When the system ran against more than 2,000 random optimization problems, it came to the correct solution 98 percent of the time.</p> <p>Previously, researchers at Stanford University <a href=";keytype=ref&amp;siteid=sci">demonstrated an Ising machine</a> that uses lasers and electronics to solve optimization problems. That work revealed the potential for a significant speedup over digital computing although, according to Chou, the system may be difficult and costly to scale to larger sizes. The goal of finding a simpler alternative ignited the laboratory's research.&nbsp;</p> <p><strong>Scaling up</strong></p> <p>The individual oscillator circuit the team used in their demonstration is similar to circuitry found inside cellphones or Wi-Fi routers. One addition they've made is a crossbar architecture that allows all of the oscillators in the circuit to be directly coupled to each other. "We have found an architecture that is both scalable to manufacture and can enable full connectivity to thousands of oscillators," Chou says. A fully connected system allows it to easily be mapped to a wide variety of optimization problems.&nbsp;</p> <p>"This work from Lincoln Laboratory makes innovative use of a crossbar architecture in its construction of an analog-electronic Ising machine," says Peter McMahon, an assistant professor of applied and engineering physics at Cornell University who was not involved in this research. "It will be interesting to see how future developments of this architecture and platform perform." &nbsp;</p> <p>The laboratory's prototype Ising machine uses four oscillators. The team is now working out a plan to scale the prototype to larger numbers of oscillators, or "nodes," and fabricate it on a printed circuit board. "If we can get to, say, 500 nodes, there is a chance we can start to compete with existing computers, and at 1,000 nodes we might be able to beat them," Bramhavar says.</p> <p>The team sees a clear path forward to scaling up because the technology is based on standard electronic components. It's also extremely cheap. All the parts for their prototype can be found in a typical undergraduate electrical engineering lab and were bought online for about $20.</p> <p>"What excites me is the simplicity," Bramhavar adds. "Quantum computers are expected to demonstrate amazing performance, but the scientific and engineering challenges required to scale them up are quite hard. Demonstrating even a small fraction of the performance gains envisioned with quantum computers, but doing so using hardware from the existing electronics industry, would be a huge leap forward. Exploiting the natural behavior of these circuits to solve real problems presents a very compelling alternative for what the next era of computing could be."</p> An analog circuit solves combinatorial optimization problems by using oscillators' natural tendency to synchronize. The technology could scale up to solve these problems faster than digital computers.Image: Bryan Mastergeorge Lincoln Laboratory, Computing, electronics, Research, Computer science and technology, Mathematics At MIT, 268 take part in world&#039;s largest math competition for girls Now in its 11th year, the Math Prize for Girls has been hosted by MIT nine times. Mon, 21 Oct 2019 14:10:01 -0400 Fernanda Ferreira | School of Science <p>Three young women are throwing questions, rapid fire, at Ken Fan PhD ’95, the founder of Girl’s Angle, a non-profit mathematics club for girls.</p> <p>“Is it rational?”</p> <p>“No.”</p> <p>“Is it transcendental?”</p> <p>“Yes.”</p> <p>&nbsp;“Is it pi?”</p> <p>“No.”</p> <p>“Is it e?”</p> <p>“No.”</p> <p>&nbsp;“Is it some combination of pi and e?”</p> <p>“Too vague.”</p> <p>They’re trying to solve a collaborative puzzle created by Fan, and one of the steps involves asking him yes-or-no questions in order to figure out the number they need for the next step. After a few tries, they’ve got it (it’s -e/2), and they return to the rest of the group working to find the combination for the locked box that contains their prize. According to Fan, the prize is a bag of candy, but he hopes the girls will take something else away from the game: an understanding that math is collaborative and fun.</p> <p><strong>Playing 20 questions</strong></p> <p>The girls are some of the 268 middle and high school math enthusiasts who descended on campus the weekend of Oct. 12-13 for the Math Prize for Girls event. The mathematics competition, hosted at MIT for the ninth time, is the world’s largest math prize for young women high school age or younger. On Sunday, they settled into their seats to solve 20 math questions, but the night before they bonded over cake and games.</p> <p>Game night happened in the Lobdell Dining Hall in the Stratton Student Center (Building W20) with activities designed to integrate the contestants. At one end of the dining hall, Meena Boppana, a longtime volunteer of Math Prize, guided a speed-dating-style get-to-know-you station, in which contestants and alumnae of the program could become quickly acquainted. Throughout the hall, sitting at tables or on the floor, contestants put together red, 3D rhombic hexecontahedron puzzles, then stacked these into tall towers. At another station, Jeannine Mosely PhD ’84, a software engineer at Akamai Technologies, demonstrated how to create one of her curved-edge origami designs.</p> <p>For Maria De Vuono-Homberg, the associate director of Math Prize for Girls, the focus on community building is what makes this competition special. “Out of almost 300 girls, maybe 10 will get a trophy, another 25 will get an honorable mention, but that’s not why they’re here,” says De Vuono-Homberg. “They’re here to spend time together.”</p> <p>“I like it because it’s bringing together a lot of people with similar interests and backgrounds. And it’s all girls, which is not something you see all the time,” says one contestant from Canada. “When you think of how many different places these people are coming from, it’s just really nice,” she adds.</p> <p>According to De Vuono-Homberg, when contestants fill out the post-competition survey, they highlight the importance of being surrounded by people who share their love of math. And for her, that’s the whole point of the competition. “At Math Prize, your right to belong is never questioned,” says De Vuono-Homberg.</p> <p><strong>Growing a supportive network</strong></p> <p>Math Prize for Girls was started 11 years ago by Ravi Boppana PhD ’86, a research affiliate in the Department of Mathematics, and Arun Alagappan, the founder of Advantage Testing, to address the gender gap in math. Inspired by their daughters — Boppana has one (longtime volunteer Meena), and Alagappan has three — they created the competition to celebrate girls’ love of math and to build a community of alumnae that encourage women to pursue math. “Ravi and I founded Math Prize, knowing that as long as they were supported, women would persevere,” said Alagappan at the award ceremony.</p> <p>Justina Yang ’19, attended Math Prize for Girls for four years when she was in high school, but it was when she became an alumna volunteer that she realized the importance of the supportive network it builds. “Over the past few years, I’ve grown to really enjoy and value talking to people who come to Math Prize,” she says. For Yang, one of the reasons she continues to volunteer at Math Prize is the hope that she can be of use to the contestants, many of whom are applying to college and figuring out their next steps.</p> <p>Throughout the competition, the collaborative nature of math was highlighted. “We know that math is inherently collaborative,” says Alagappan, with breakthroughs coming from teams and through colleagues that help mathematicians approach problems in new ways. Giving the Maryam Mirzakhani Keynote Lecture — named in honor of Mirzakhani, the first woman to win the Fields Medal — Gigliola Staffilani, the Abby Rockefeller Mauze Professor of Mathematics, compared mathematical proofs to a pile of laundry. “When I look at a problem, it’s like a pile of laundry — it’s a mess,” she says. But as each small item is folded, the problem becomes clearer. “And then there are the big things, like sheets, for which you need help, and that involves collaboration,” Staffilani explains.</p> <p>After Staffilani’s keynote lecture, the top 10 awards were listed. The first prize, with 14 out of 20 questions answered correctly, went to Jessica Wan, an 8th grader from Puerto Rico, who also received the Youth Prize, which is awarded to the highest-scoring contestant in 9th grade and below. A full list of the winners and honorable mentions can be found at the <a href="">Art of Problem Solving website</a>.</p> <p>As contestants collected their bags and said their goodbyes, the words of Emma Kerwin ’19, Math Girls alumna and awards ceremony emcee, went with them: “We are proud of you, we believe in you, and we truly expect great things from you.”&nbsp;</p> Winners of the 11th Math Prize for Girls, including first-prize winner 8th grader Jessica Wan, stand with Arun Algappan, founder of Advantage Testing (far left) and MIT Professor Gigliola Staffilani (far right)Photo: Fernanda FerreiraSchool of Science, Mathematics, Special events and guest speakers, Women in STEM, K-12 education, STEM education, Diversity and inclusion, Contests and academic competitions Alan Edelman recognized with 2019 IEEE Computer Society Sidney Fernbach Award Edelman&#039;s Julia programming language was recognized for solving large computational problems for high-performance computers. Thu, 17 Oct 2019 14:40:01 -0400 Sandi Miller | Department of Mathematics <p>Applied mathematics Professor <a href="">Alan Edelman</a> has been selected to receive the 2019&nbsp;<a href="">IEEE Computer Society Sidney Fernbach Award</a>.</p> <p>Edelman was cited “for outstanding breakthroughs in high performance computing, linear algebra, and computational science and for contributions to the Julia programming language.”</p> <p>One of the IEEE Computer Society's highest honors, the Sidney Fernbach Award recognizes outstanding contributions in the application of high-performance computers (HPC) using innovative approaches.</p> <p>Edelman works on numerical computation, linear algebra, random matrix theory, and geometry, and says that he loves algorithms, theorems, compilers, DSLs, and old-fashioned performance tuning. But a lifelong goal has been improving HPC research.</p> <p>“Julia was invented to prove that HPCs’ biggest challenges could be solved with language,” says Edelman, who leads the Julia laboratory in the Computer Science and Artificial Intelligence Laboratory (CSAIL), and is chief scientist at Julia Computing. “Still, there is so&nbsp;much work to do.”</p> <p>Edelman’s interest in HPC emerged shortly after he arrived at MIT in the 1980s to earn his doctorate in applied mathematics. He said he “learned many lost lessons moonlighting” at Thinking Machines, where he started thinking about how “breakthroughs in HPC could come from raising the levels of abstraction through high-level languages that are built from the ground up for performance and productivity.”</p> <p>“HPC had missed out for too long on the key intellectual ingredient that would make all the difference: language. The ‘one true goal’ for HPC is the number of users. Performance, productivity, scalability, reproducibility, composability, and other obvious and non-obvious metrics are subsumed by this ‘prime directive.’”</p> <p>Edelman, who came back to MIT as faculty in 1993, eventually teamed up&nbsp;with <a href="" target="_blank">Jeff Bezanson</a> PhD ’15, <a href="">Stefan Karpinski</a>, and&nbsp;<a href="">Viral B. Shah</a> to create a programming language that they called <a href="">Julia</a>. This language was designed to help researchers write high-level code in an intuitive syntax and produce code with the speed of production programming languages.</p> <p>The free and open-source Julia 1.0 was released in 2018. Today, the Julia project has over 800 open source contributors, 2,000 registered packages, and over 10 million downloads. It is used in over 1,500 universities, including MIT, for solving difficult and large-scale problems in areas such as <a href="">climate modeling</a>, <a href="">scientific machine learning</a>, and <a href="">medicine</a>. Julia is also used by companies such as BlackRock, Capital One, Intel, Cisco, and Netflix, and by government agencies such as the Federal Aviation Administration, NASA, and the Federal Reserve Bank of New York.</p> <p>The Julia creators received the <a href="" target="_blank">2019 James H. Wilkinson Prize for Numerical Software</a>. In addition, Edelman has also received the Householder Prize, the Chauvenet Prize, and the Charles Babbage Prize. &nbsp;<strong>&nbsp;</strong></p> <p>Edelman will receive his award, which consists of a certificate and a $2,000 honorarium, on Nov. 19 at the Supercomputing 2019 Conference awards plenary session in Denver, Colorado.</p> Applied mathematics Professor Alan Edelman has been selected to receive the 2019 IEEE Computer Society Sidney Fernbach Award. Mathematics, School of Science, Computer science and technology, Awards, honors and fellowships, Faculty, Computer Science and Artificial Intelligence Laboratory (CSAIL) Meet the 2019-20 MLK Visiting Professors and Scholars Six scholars and professors are spending this academic year in engagement with the MIT community. Tue, 08 Oct 2019 16:40:44 -0400 Beatriz Cantada | Institute Community and Equity Office <p>Founded in 1990, the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program honors the life and legacy of Martin Luther King by increasing the presence of, and recognizing the contributions of, underrepresented minority scholars at MIT. MLK Visiting Professors and Scholars enhance their scholarship through intellectual engagement with the MIT community and enrich the cultural, academic, and professional experience&nbsp;of students. The program hosts between four and eight scholars each year with financial and institutional support from the Office of the Provost and oversight from the Institute Community and Equity Office. Six scholars are visiting MIT this academic year as part of the program.</p> <p><a href="" target="_blank">Kasso Okoudjou</a> is returning for a second year as an MLK Visiting Professor in the Department of Mathematics. Originally from Benin, he moved to the United States in 1998 and earned a PhD in mathematics from Georgia Tech. Okoudjou joins MIT from the University of Maryland College Park, where he is a professor. His research interests include applied and pure harmonic analysis, especially time-frequency and time-scale analysis; frame theory; and analysis and differential equations on fractals. He is interested in broadening the participation of underrepresented minorities in (undergraduate) research in the mathematical sciences.</p> <p><a href="">Matthew Schumaker</a> joins MIT for another year in the Music and Theater Arts Section within the School of Humanities, Arts, and Social Sciences. Schumaker received his doctorate in music composition from the University of California at Berkeley. At MIT, he teaches a new course, 21M.380 (<a href="">Composing for Solo Instrument and Live Electronics</a>), a&nbsp;hands-on music technology composition seminar combining instrumental writing with real-time computer music. Additionally, <a href="">The Radius Ensemble</a> in Cambridge, Massachusetts has commissioned Schumaker to write a new piece of&nbsp;music that seeks to translate into music the vibrant, curved gestures and slashed markings in the abstract landscapes of celebrated Ethiopian-born painter Julie Mehretu.</p> <p><a href="">Jamie Macbeth</a> is visiting from Smith College, where he is an assistant professor in computer science. He received his PhD in computer science from University of California at Los Angeles. Although this is his first year as an MLK Visiting Scholar, he is not new to MIT, since he has been a visiting scientist since 2017. He is hosted by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Macbeth’s research is focused on building and studying intelligent computing systems that demonstrate a human-like capability for in-depth understanding and production of natural language, and thus can achieve richer interactions with human users. He is especially keen on building systems that decompose the meaning of language into complex conceptual structures that reflect humans’ embodied cognition, memory, imagery&nbsp;and knowledge about social situations.</p> <p><a href="">Ben McDonald</a> has been a postdoc in the Department of Chemistry since 2018 and is now an MLK Visiting Scholar. McDonald received his PhD in synthetic organic chemistry from Northwestern University. His research focused on the total synthesis of flavonolignan natural products and the development of reverse-polarity carbon-carbon bond forming reactions. As a member of the department’s Chemistry Alliance for Inclusion and Diversity, he is focused on advancing diversity, equity and inclusion efforts. One of the initiatives he seeks to establish is a summer research program, which recruits talented future scientists from underrepresented backgrounds.</p> <p><a href="">Tina Opie</a> is an associate professor in the Management Division at Babson College. Opie obtained her PhD in management (with a concentration in organizational behavior) from New York University’s Stern School of Business. As an MLK Visiting Scholar in MIT Sloan School of Management, along with access to MIT’s Behavioral Research Lab, she is conducting research to develop the construct of <a href="" target="_blank">Shared Sisterhood</a>. “Shared Sisterhood examines how high-quality relationships (e.g., relationships characterized by trust, emotional vulnerability) between black, white, and Latinx women at work facilitate workplace inclusion and equity.” Though her work has a specific focus, people of all genders and racioethnic backgrounds can be “sisters” and can contribute to fostering a more inclusive work environment. Opie established Opie Consulting Group, a diversity-and-inclusion consultancy that incorporates Shared Sisterhood in creating inclusive workplaces. &nbsp;</p> <p><a href="">Rhonda Williams</a>, an MLK Visiting Professor hosted by the Department of History, joins MIT from Vanderbilt University, where she was recently appointed the John L. Seigenthaler Chair in American History. She is the founder of the <a href="">Social Justice Institute</a> at Case Western Reserve University. Her essay titled <a href="">“Black Women Who Educate for Justice and Put Their Time, Lives, and Spirits on the Line"</a> was recently published in "Black Women And Social Justice Education: Legacies and Lessons" (2019, SUNY Press). On Oct. 25, Williams will deliver a social justice-related performance-lecture called “<a href="" target="_blank">The Things That Divide Us: Meditations</a>” at MIT. In spring 2020, she will facilitate a social justice workshop for students, faculty and staff.</p> <p>For more information about our scholars and the program, visit <a href="" target="_blank"></a>.</p> The 2019-2020 MLK Visiting Scholars and Professors: (l-r) Tina Opie, Matthew Schumaker, Rhonda Williams, Ben McDonald, Kasso Okoudjou, and Jamie Macbeth.Photo: Beatriz CantadaCommunity, Faculty, Staff, Diversity and inclusion, Mathematics, School of Science, Music and theater arts, School of Humanities Arts and Social Sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Chemistry, Sloan School of Management, History, School of Engineering Victor Kac elected to the Accademia Nationale dei Lincei Victor Kac elected to the Accademia Nationale dei Lincei Mathematics professor will join Galileo and Einstein as a member of the world&#039;s oldest science academy. Mon, 07 Oct 2019 15:45:01 -0400 Sandi Miller | Department of Mathematics <p>In 1977, math professor <a href="">Victor Kac</a> was a refugee from the Soviet Union living in Rome. As he was awaiting a U.S. visa so he could begin teaching at MIT, he met <a href="">Claudio Procesi,</a> an algebra professor at the&nbsp;Sapienza University of Rome, and other Italian mathematicians who set him up with a room in a family member’s home and helped him with paid talks in Pisa and with traveling throughout Italy. He said that those professors became his lifelong friends.</p> <p>More than 40 years later, he will return to Rome in November to be inducted as a&nbsp;foreign member of the <a href="">Accademia Nationale dei Lincei</a>, the Italian National Academy of Sciences, which is the oldest science academy in the world.</p> <p>Founded in 1603, the Italian institution counts Galileo Galilei as among its first members. Other distinguished members have included Niels&nbsp;Bohr, Albert Einstein, and Erwin Schrodinger.</p> <p>“I was quite surprised and profoundly honored,” says Kac, who joins only 20 other Accademia foreign members in math, such as Fields medalists Pierre Deligne, Pierre-Louis Lions, David Mumford, and Shing-Tung Yau.</p> <p>Kac works in several areas of algebra and mathematical physics related to symmetries. “Victor’s development of Kac-Moody algebras has continued implications for mathematics, as well as theoretical physics research,” says <a href="">Michael Sipser</a>, dean of the MIT <a href="">School of Science</a> and the Donner Professor of Mathematics. “His work developing an algebraic theory of integrable systems, as well as his theory of Lie superalgebras, make him more than deserving of this extraordinary honor.”</p> <p>The academy is composed of 540 members elected on the basis of their scientific merit by its national members: 180 Italian members, 180 Italian corresponding members, and 180 foreign members.</p> <p>“To the best of my knowledge, Victor is the first MIT&nbsp;mathematician to receive this honor,” says Michel Goemans, head of the Department of Mathematics. Two other MIT professors are members of the academy: Department of Brain and Cognitive Sciences Professor Emeritus <a href="">Emilio Bizzi</a>, who was born in Rome, and Palermo native <a href="">Silvio Micali</a>, Ford Professor of Engineering in the Computer Science and Artificial Intelligence Laboratory. So is Kac’s friend Procesi.</p> MIT professor of mathematics Victor Kac will be inducted into the Accademia Nationale dei Lincei in November.Awards, honors and fellowships, Mathematics, Faculty, Europe Computing in Earth science: a non-linear path UROP student Sonia Reilly studies the math of machine learning to improve predictions of natural disasters. Wed, 11 Sep 2019 14:40:01 -0400 Laura Carter | School of Science <p>Machine learning is undeniably a tool that most disciplines like to have in their toolbox. However, scientists are still investigating the limits and barriers to incorporating machine learning into their research. Junior Sonia Reilly spent her summer opening up the machine learning black box to better understand how information flows through neural networks as part of the <a href="">Undergraduate Research Opportunities Program</a> (UROP). Her project, which investigates how machine learning works with the intention of improving its application to the observation of natural phenomena, was overseen by Sai Ravela in the <a href="">Department of Earth, Atmospheric and Planetary Sciences </a>(EAPS). As a major in Course 18C (Mathematics with Computer Science), Reilly is uniquely equipped to help investigate these connections.</p> <p>“In recent years, deep learning has become an immensely popular tool in all kinds of research fields, but the mathematics of how and why it is so effective is still very poorly understood,” says Reilly. “Having that knowledge will enable the design of better-performing learning machines.” To do that, she looks more closely at how the algorithms evolve to produce their final most-probable conclusions, with the end goal of providing insights on information flow, bottlenecks, and maximizing gain from neural networks.</p> <p>“We don’t want to be drowning in big data. On the contrary, we want to transform big data into perhaps what we might call smart data,” Ravela says of how machine learning must proceed. “The end goal is always a sensing agent that gathers data from our environment, but one that is knowledge-driven and does just enough work to gather just enough information for meaningful inferences.”</p> <p>For Ravela, who leads the <a href="">Earth Signals and Systems Group</a> (ESSG), better-performing learning machines means more robust early predictions of potential disasters. His group’s research lies largely in how the Earth works as a system, primarily focusing on climate and natural hazards. They observe natural phenomena to produce effective predictive models for dynamic natural processes, such as hurricanes, clouds, volcanoes, earthquakes, glaciers, and wildlife conservation strategies, as well as making advances in engineering and learning itself.</p> <p>“In all these projects, it’s impossible to gather dense data in space and time. We show that actively mining the environment through a systems analytic approach is promising,” he says. Ravela recently delivered his group’s latest work — including Reilly’s contributions — to the Association of Computing Machinery’s special interest group on knowledge discovery and data mining (SIGKDD 2019) in early August. He teaches an “<a href="">infinite course</a>” with a duology of classes taught in spring and fall semesters that provides an overview of machine learning foundations for natural systems science, which anyone can follow along with online.</p> <p>According to Ravela, if Reilly is to succeed at advancing the mathematical basis for computational learning models, she will be one of the “early pioneers of learning that can be explained,” an achievement that can provide a promising career path.</p> <p>That is ideal for Reilly’s goals of obtaining a PhD in mathematics after graduating from MIT and remaining a contributor to research that can positively impact the world. She’s starting with cramming as much research as she can manage into her schedule over her final two undergraduate years at MIT, including her experience this summer.</p> <p>Although this was Reilly’s first UROP experience, it is her second time undertaking a research project that blends mathematics, computer science, and Earth science. Previously, at the Johns Hopkins University Applied Physics Laboratory, Reilly helped develop signal processing techniques and software that would improve the retrieval of useful climate change information from low-quality satellite data.</p> <p>“I’ve always wanted to be part of an interdisciplinary research environment where I could use my knowledge of math to contribute to the work of scientists and engineers,” Reilly says of working within EAPS. “It’s encouraging to see that type of environment and get a taste of what it would be like to work in one.”</p> <p>Ravela explains that the ESSG is fond of the mutually beneficial inclusion of UROP students. “For me, UROPs are better than grad student and postdocs if, and only if, one can create the right-sized questions for them to run with. But then they run the fastest and are the most clever of all.” He says he feels the UROP program is invaluable and could be beneficial for all students to incorporate, as it offers a chance to learn about other fields and interdisciplinary research, as well as how to incorporate what they learn into tangible results.</p> <p>For Reilly, research builds on her foundation obtained from taking classes at MIT, which are a controlled and predictable environment, she says, “but research is nowhere near so linear.” She has relied on her foundation of mathematics and computer science from her courses during her UROP experience while having to learn how to connect and apply them to new fields and to consider topics often outside an undergraduate education. “It often feels like every step I take requires me to learn about an entirely new field of mathematics, and it’s difficult to know where to start. I definitely feel lost sometimes, but I’m also learning an incredible amount.”</p> Course 18C student Sonia Reilly (right) stands with her UROP research advisor, Sai Ravela, in the Department of Earth, Atmospheric and Planetary Sciences, where she worked on a machine learning project this summer. Photo: Lauren HinkelUndergraduate Research Opportunities Program (UROP), EAPS, Students, Mathematics, School of Science, Computer science and technology, Undergraduate, Machine learning, Artificial intelligence, Natural disasters The answer to life, the universe, and everything Mathematics researcher Drew Sutherland helps solve decades-old sum-of-three-cubes puzzle, with help from &quot;The Hitchhiker&#039;s Guide to the Galaxy.&quot; Tue, 10 Sep 2019 15:45:01 -0400 Sandi Miller | Department of Mathematics <p>A team led by Andrew Sutherland of MIT and Andrew Booker of Bristol University has solved the final piece of a famous 65-year old math puzzle with an answer for the most elusive number of all: 42.</p> <p>The number 42 is especially <a href="" target="_blank">significant to fans</a> of science fiction novelist Douglas Adams’ <em>“</em>The Hitchhiker’s Guide to the Galaxy,<em>”</em> because that number is the answer given by a supercomputer to “the Ultimate Question of Life, the Universe, and Everything.”&nbsp;&nbsp;&nbsp;</p> <p>Booker also wanted to know the answer to 42. That is, are there three cubes whose sum is 42?</p> <p>This sum of three cubes puzzle, first set in 1954 at the University of Cambridge and known as the Diophantine Equation x<sup>3</sup>+y<sup>3</sup>+z<sup>3</sup>=k, challenged mathematicians to find solutions for numbers 1-100. With smaller numbers, this type of equation is easier to solve: for example, 29 could be written as 3<sup>3</sup> + 1<sup>3</sup> + 1<sup>3</sup>, while 32 is unsolvable. All were eventually solved, or proved unsolvable, using various techniques and supercomputers, except for two numbers: 33 and 42.</p> <p>Booker devised an ingenious <a href="" target="_blank">algorithm</a> and spent weeks on his university’s supercomputer when he <a href="" target="_blank">recently came up with a solution for 33</a>. But when he turned to solve for 42, Booker found that the computing needed was an order of magnitude higher and might be beyond his supercomputer’s capability. Booker says he received many offers of help to find the answer, but instead he turned to his friend Andrew <a href="" target="_blank">"Drew" Sutherland</a>, a principal research scientist in the Department of Mathematics. “He’s a world’s expert at this sort of thing,” Booker says.</p> <p>Sutherland, whose specialty includes massively parallel computations, broke the record in 2017 for the largest <a href="">Compute Engine cluster</a>, with 580,000 cores on <a href="">Preemptible Virtual Machines</a>, the largest known high-performance computing cluster to run in the public cloud.</p> <p>Like other computational number theorists who work in arithmetic geometry, he was aware of the “sum of three cubes” problem. And the two had worked together before, helping to build the <a href="" target="_blank">L-functions and Modular Forms Database</a> (LMFDB), an online atlas of mathematical objects related to what is known as the Langlands Program. “I was thrilled when Andy asked me to join him on this project,” says Sutherland.</p> <p>Booker and Sutherland discussed the algorithmic strategy to be used in the search for a solution to 42. As Booker found with his solution to 33, they knew they didn’t have to resort to trying all of the possibilities for x, y, and z.</p> <p>“There is a single integer parameter, d, that determines a relatively small set of possibilities&nbsp;for x, y, and z such that the absolute&nbsp;value of z is below a chosen search bound B,” says Sutherland. “One then&nbsp;enumerates values for d and checks each of the possible x, y, z associated to d. In the attempt to crack 33, the search bound B was 10<sup>16</sup>, but this B&nbsp;turned out to be too small to crack 42; we instead used B = 10<sup>17</sup> (10<sup>17</sup> is 100 million billion).</p> <p>Otherwise, the main difference between&nbsp;the search for 33 and the search for 42 would be the size of the search and&nbsp;the computer platform used. Thanks to a generous offer from UK-based <a href="">Charity Engine</a>, Booker and Sutherland were able to tap into the computing power from over 400,000 volunteers’ home PCs, all around the world, each of which was assigned a range of values for d.&nbsp;The&nbsp;computation on each PC runs in the background so the owner can still&nbsp;use their PC for other tasks.</p> <p>Sutherland is also a fan of Douglas Adams, so the project was irresistible.</p> <p>The method of using Charity Engine is similar to part of the plot surrounding the number 42 in the "Hitchhiker" novel: After Deep Thought’s answer of 42 proves unsatisfying to the scientists, who don’t know the question it is meant to answer, the supercomputer decides to compute the Ultimate Question by building a supercomputer powered by Earth … in other words, employing a worldwide massively parallel computation platform.</p> <p>“This is another reason I really liked running this computation&nbsp;on Charity Engine — we actually did use a planetary-scale computer to settle a longstanding open question whose answer is 42.”</p> <p>They ran a number of computations at a lower capacity to test both their&nbsp;code and the Charity Engine network. They then used a number of&nbsp;optimizations and adaptations to make the code better suited for a&nbsp;massively distributed computation, compared to a computation run on a single supercomputer, says Sutherland.</p> <p>Why couldn't Bristol's supercomputer solve this problem?</p> <p>“Well, any computer *can* solve the problem, provided you are willing&nbsp;to wait long enough, but with roughly half a million PCs working on the problem in parallel (each with multiple cores), we were able to complete the computation much more quickly than we could have using the Bristol machine (or any of the machines here at MIT),” says Sutherland. &nbsp;</p> <p>Using the Charity Engine network is also more energy-efficient. “For the most part, we are using computational resources that would otherwise go to waste,” says Sutherland.&nbsp;“When you're sitting at your computer reading an email or working on a spreadsheet, you are using only a tiny fraction of the CPU resource available, and the Charity Engine application, which is based on the Berkeley Open Infrastructure for Network Computing (BOINC), takes advantage of this. As a result, the carbon footprint of this computation — related to the electricity our computations caused the PCs in the network to use&nbsp;above and beyond what they would have used, in any case — is lower than it would have been if we had used a supercomputer.”</p> <p>Sutherland and Booker ran the computations over several months, but the final successful run was completed in just a few&nbsp;weeks. When the email from Charity Engine arrived, it provided the first solution to x<sup>3</sup>+y<sup>3</sup>+z<sup>3</sup>=42:</p> <p>42 = (-80538738812075974)^3 + 80435758145817515^3 + 12602123297335631^3</p> <p>“When I heard the news, it was definitely a fist-pump moment,” says Sutherland.&nbsp;“With these&nbsp;large-scale computations you pour a lot of time and energy into&nbsp;optimizing the implementation, tweaking the parameters, and then testing&nbsp;and retesting the code over weeks and months, never really knowing if&nbsp;all the effort is going to pay off, so it is extremely satisfying when&nbsp;it does.”</p> <p>Booker and Sutherland say there are 10 more numbers, from 101-1000, left to be solved, with the next number being 114.</p> <p>But both are more interested in a simpler but computationally more challenging puzzle: whether there are more answers for the sum of three cubes for 3.</p> <p>“There are four very easy solutions that were known to the mathematician Louis J. Mordell, who famously wrote in 1953, ‘I do not know anything about the integer solutions of x<sup>3</sup> + y<sup>3</sup> + z<sup>3</sup> = 3 beyond the existence of the four triples (1, 1, 1), (4, 4, -5), (4, -5, 4), (-5, 4, 4); and it must be very difficult indeed to find out anything about any other solutions.’ This quote motivated a lot of the interest in the sum of three cubes problem, and the case k=3 in particular. While it is conjectured that there should be infinitely many solutions, despite more than 65 years of searching we know only the easy solutions that were already known to Mordell. It would be very exciting to find another solution for k=3.”</p> MIT mathematician Andrew "Drew" Sutherland solved a 65-year-old problem about 42.Image: Department of MathematicsMathematics, School of Science, Research, Staff Daniel Freedman wins Special Breakthrough Prize in Fundamental Physics MIT professor emeritus will share $3 million prize with Sergio Ferrara and Peter van Nieuwenhuizen for discovery of supergravity. Tue, 06 Aug 2019 10:00:41 -0400 MIT News Office <p>Daniel Z. Freedman, professor emeritus in MIT’s departments of Mathematics and Physics, has been awarded the Special Breakthrough Prize in Fundamental Physics. He shares the $3 million prize with two colleagues, Sergio Ferrara of CERN and Peter van Nieuwenhuizen of Stony Brook University, with whom he developed the theory of supergravity.</p> <p>The trio is honored for work that combines the principles of supersymmetry, which postulates that all fundamental particles have corresponding, unseen “partner” particles; and Einstein's theory of general relativity, which explains that gravity is the result of the curvature of space-time.</p> <p>When the theory of supersymmetry was developed in 1973, it solved some key problems in particle physics, such as unifying three forces of nature (electromagnetism, the weak nuclear force, and the strong nuclear force), but it left out a fourth force: gravity. Freedman, Ferrara, and van Nieuwenhuizen addressed this in 1976 with their theory of supergravity, in which the gravitons of general relativity acquire superpartners called gravitinos.</p> <p>Freedman’s collaboration with Ferrara and van Nieuwenhuizen began late in 1975 at École Normale Supérior in Paris, where he was visiting on a minisabbatical from Stony Brook, where he was a professor. Ferrara had also come to ENS, to work on a different project for a week. The challenge of constructing supergravity was in the air at that time, and Freedman told Ferrara that he was thinking about it. In their discussions, Ferrara suggested that progress could be made via an approach that Freedman had previously used in a related problem involving supersymmetric gauge theories.</p> <p>“That turned me in the right direction,” Freedman recalls. In short order, he formulated the first step in the construction of supergravity and proved its mathematical consistency. “I returned to Stony Brook convinced that I could quickly find the rest of the theory,” he says. However, “I soon realized that it was harder than I had expected.”</p> <p>At that point he asked van Nieuwenhuizen to join him on the project. “We worked very hard for several months until the theory came together. That was when our eureka moment occurred,” he says.</p> <p>“Dan’s work on supergravity has changed how scientists think about physics beyond the standard model, combining principles of supersymmetry and Einstein’s theory of general relativity,” says Michael Sipser, dean of the MIT School of Science and the Donner Professor of Mathematics. “His exemplary research is central to mathematical physics and has given us new pathways to explore in quantum field theory and superstring theory. On behalf of the School of Science, I congratulate Dan and his collaborators for this prestigious award.”</p> <p>Freedman joined the MIT faculty in 1980, first as professor of applied mathematics and later with a joint appointment in the Center for Theoretical Physics. He regularly taught an advanced graduate course on supersymmetry and supergravity. An unusual feature of the course was that each assigned problem set included suggestions of classical music to accompany students’ work.&nbsp;</p> <p>“I treasure my 36 years at MIT,” he says, noting that he&nbsp; worked with “outstanding” graduate students with “great resourcefulness as problem solvers.” Freedman fully retired from MIT in 2016.</p> <p>He is now a visiting professor at Stanford University and lives in Palo Alto, California, with his wife, Miriam, an attorney specializing in public education law.</p> <p>The son of small-business people, Freedman was the first in his family to attend college. He became interested in physics during his first year at Wesleyan University, when he enrolled in a special class that taught physics in parallel with the calculus necessary to understand its mathematical laws. It was a pivotal experience. “Learning that the laws of physics can exactly describe phenomena in nature — that totally turned me on,” he says.</p> <p>Freedman learned about winning the Breakthrough Prize upon returning from a morning boxing class, when his wife told him that a Stanford colleague, who was on the Selection Committee, had been trying to reach him. “When I returned the call, I was overwhelmed with the news,” he says.</p> <p>Freedman, who holds a BA from Wesleyan and an MS and PhD in physics from the University of Wisconsin, is a former Sloan Fellow and a two-time Guggenheim Fellow. The three collaborators received the Dirac Medal and Prize in 1993, and the Dannie Heineman Prize in Mathematical Physics in 2006. He is a fellow of the American Academy of Arts and Sciences.</p> <p>Founded by a group of Silicon Valley entrepreneurs, the Breakthrough Prizes recognize the world’s top scientists in life sciences, fundamental physics, and mathematics. The Special Breakthrough Prize in Fundamental Physics honors profound contributions to human knowledge in physics. Earlier honorees include Jocelyn Bell Burnell; the <a href="">LIGO research team</a>, including MIT Professor Emeritus Rainer Weiss; and Stephen Hawking. &nbsp;</p> Daniel FreedmanImage courtesy of Daniel FreedmanPhysics, School of Science, Faculty, Awards, honors and fellowships, Center for Theoretical Physics, Laboratory for Nuclear Science, Mathematics Mathematical insights through collaboration and perseverance “Patience is important for our subject,” says math professor Wei Zhang. “You’re always making infinitesimal progress.” Sun, 28 Jul 2019 00:00:00 -0400 Jonathan Mingle | MIT News correspondent <p>Wei Zhang’s breakthrough happened on the train. He was riding home to New York after visiting a friend in Boston, during the last year of his PhD studies in mathematics at Columbia University, where he was focusing on L-functions, an important area of number theory.</p> <p>“All of a sudden, things were linked together,” he recalls, about the flash of insight that allowed him to finish a key project related to his dissertation. “Definitely it was an ‘Aha!’ moment.”</p> <p>But that moment emerged from years of patient study and encounters with other mathematicians’ ideas. For example, he had attended talks by a certain faculty member in his first and third years at Columbia, but each time he thought the ideas presented in those lectures wouldn’t be relevant for his own work.</p> <p>“And then two years later, I found this was exactly what I needed to finish a piece of the project!” says Zhang, who joined MIT two years ago as a professor of mathematics.</p> <p>As Zhang recalls, during that pivotal train ride his mind had been free to wander around the problem and consider it from different angles. With this mindset, “I can have a more panoramic way of putting everything into one piece. It’s like a puzzle — when you close your eyes maybe you can see more. And when the mind is trying to organize different parts of a story, you see this missing part.”</p> <p>Allowing time for this panoramic view to come into focus has been critical throughout Zhang’s career. His breakthrough on the train 11 years ago led him to propose a set of conjectures that he has just now solved in a recent paper.</p> <p>“Patience is important for our subject,” he says. “You’re always making infinitesimal progress. All discovery seems to be made in one moment. But without the preparation and long-time accumulation of knowledge, it wouldn’t be possible.”</p> <p><strong>An early and evolving love for math</strong></p> <p>Zhang traces his interest in math back to the fourth grade in his village school in a remote part of China’s Sichuan Province. “It was just pure curiosity,” he says. “Some of the questions were so beautifully set up.”</p> <p>He started participating in math competitions. Seeing his potential, a fifth-grade math teacher let Zhang pore over an extracurricular book of problems. “Those questions made me wonder how such simple solutions to seemingly very complicated questions could be possible,” he says.</p> <p>Zhang left home to attend a high school 300 miles away in Chengdu, the capital city of Sichuan. By the time he applied to study at Peking University in Beijing, he knew he wanted to study mathematics. And by his final year there, he had decided to pursue a career as a mathematician.</p> <p>He credits one of his professors with awakening him to some exciting frontiers and more advanced areas of study, during his first year. At that time, around 2000, the successful proof of Fermat’s Last Theorem by Andrew Wiles five years earlier was still relatively fresh, and reverberating through the world of mathematics. “This teacher really liked to chat,” Zhang says, “and he explained the contents of some of those big events and results in a way that was accessible to first-year students.”</p> <p>“Later on, I read those texts by myself, and I found it was something I liked,” he says. “The tools being developed to prove Fermat’s Last Theorem were a starting point for me.”</p> <p>Today, Zhang gets to cultivate his own students’ passion for math, even as his teaching informs his own research. “It has happened more than once for me, that while teaching I got inspired,” he says. “For mathematicians, we may understand some sort of result, but that doesn’t mean we actually we know how to prove them. By teaching a course, it really helps us go through the whole process. This definitely helps, especially with very talented students like those at MIT.”</p> <p><strong>From local to global information</strong></p> <p>Zhang’s core area of research and expertise is number theory, which is devoted to the study of integers and their properties. Broadly speaking, Zhang explores how to solve equations in integers or in rational numbers. A familiar example is a Pythagorean triple (a<sup>2</sup>+b<sup>2</sup>=c<sup>2</sup>).</p> <p>“One simple idea is try to solve equations with modular arithmetic,” he says. The most common example of modular arithmetic is a 12-hour clock, which counts time by starting over and repeating after it reaches 12. With modular arithmetic, one can compile a set of data, indexed, for example, by prime numbers.</p> <p>“But after that, how do you return to the initial question?” he says. “Can you tell an equation has an integer solution by collecting data from modular arithmetic?” Zhang investigates whether and how an equation can be solved by restoring this local data to a global piece of information — like finding a Pythagorean triple.</p> <p>His research is relevant to an important facet of the Langlands Program — a set of conjectures proposed by mathematician Robert Langlands for connecting number theory and geometry, which some have likened to a kind of “grand unified theory” of mathematics.</p> <p><strong>Conversations and patience</strong></p> <p>Bridging other branches of math with number theory has become one of Zhang’s specialties.</p> <p>In 2018, he <a href="">won</a> the New Horizons in Mathematics Breakthroughs Prize, a prestigious award for researchers early in their careers. He shared the prize with his old friend and undergraduate classmate, and current MIT colleague, Zhiwei Yun, for their joint <a href="">work</a> on the Taylor expansion of L-functions, which was hailed as a major advance in a key area of number theory in the past few decades.</p> <p>Their project grew directly out of his dissertation research. And that work, in turn, opened up new directions in his current research, related to the arithmetic of elliptic curves. But Zhang says the way forward wasn’t clear until five years — and many conversations with Yun — later.</p> <p>“Conversation is important in mathematics,” Zhang says. “Very often mathematical questions can be solved, or at least progress can be made, by bringing together people with different skills and backgrounds, with new interpretations of the same set of facts. In our case, this is a perfect example. His geometrical way of thinking about the question was exactly complementary to my own perspective, which is more number arithmetic.”</p> <p>Lately, Zhang’s work has taken place on fewer train rides and more flights. He travels back to China at least once a year, to visit family and colleagues in Beijing. And when he feels stuck on a problem, he likes to take long walks, play tennis, or simply spend time with his young children, to clear his mind.</p> <p>His recent solution of his own conjecture has led him to contemplate unexplored terrain. “This opened up a new direction,” he says. “I think it’s possible to finally get some higher-dimensional solutions. It opens up new conjectures.”</p> Wei ZhangImage: Jake BelcherFaculty, Mathematics, Profile, School of Science, China Biologists and mathematicians team up to explore tissue folding An algorithm developed to study the structure of galaxies helps explain a key feature of embryonic development. Thu, 25 Jul 2019 11:00:00 -0400 Anne Trafton | MIT News Office <p>As embryos develop, they follow predetermined patterns of tissue folding, so that individuals of the same species end up with nearly identically shaped organs and very similar body shapes.</p> <p>MIT scientists have now discovered a key feature of embryonic tissue that helps explain how this process is carried out so faithfully each time. In a study of fruit flies, they found that the reproducibility of tissue folding is generated by a network of proteins that connect like a fishing net, creating many alternative pathways that tissues can use to fold the right way.</p> <p>“What we found is that there’s a lot of redundancy in the network,” says Adam Martin, an MIT associate professor of biology and the senior author of the study. “The cells are interacting and connecting with each other mechanically, but you don’t see individual cells taking on an all-important role. This means that if one cell gets damaged, other cells can still connect to disparate parts of the tissue.”</p> <p>To uncover these network features, Martin worked with Jörn Dunkel, an MIT associate professor of physical applied mathematics and an author of the paper, to apply an algorithm normally used by astronomers to study the structure of galaxies.</p> <p>Hannah Yevick, an MIT postdoc, is the lead author of the study, which appears today in <em>Developmental Cell</em>. Graduate student Pearson Miller is also an author of the paper.</p> <div class="cms-placeholder-content-video"></div> <p><strong>A safety net</strong></p> <p>During embryonic development, tissues change their shape through a process known as morphogenesis. One important way tissues change shape is to fold, which allows flat sheets of embryonic cells to become tubes and other important shapes for organs and other body parts. Previous studies in fruit flies have shown that even when some of these embryonic cells are damaged, sheets can still fold into their correct shapes.</p> <p>“This is a process that’s fairly reproducible, and so we wanted to know what makes it so robust,” Martin says.</p> <p>In this study, the researchers focused on the process of gastrulation, during which the embryo is reorganized from a single-layered sphere to a more complex structure with multiple layers. This process, and other morphogenetic processes similar to fruit fly tissue folding, also occur in human embryos. The embryonic cells involved in gastrulation contain in their cytoplasm proteins called myosin and actin, which form cables and connect at junctions between cells to form a network across the tissue. Martin and Yevick had hypothesized that the network of cell connectivity might play a role in the robustness of the tissue folding, but until now, there was no good way to trace the connections of the network.</p> <p>To achieve that, Martin’s lab joined forces with Dunkel, who studies the physics of soft surfaces and flowing matter — for example, <a href="">wrinkle formation</a> and <a href="">patterns of bacterial streaming</a>. For this study, Dunkel had the idea to apply a mathematical procedure that can identify topological features of a three-dimensional structure, analogous to ridges and valleys in a landscape. Astronomers use this algorithm to identify galaxies, and in this case, the researchers used it to trace the actomyosin networks across and between the cells in a sheet of tissue.</p> <p>“Once you have the network, you can apply standard methods from network analysis — the same kind of analysis that you would apply to streets or other transport networks, or the blood circulation network, or any other form of network,” Dunkel says.</p> <p>Among other things, this kind of analysis can reveal the structure of the network and how efficiently information flows along it. One important question is how well a network adapts if part of it gets damaged or blocked. The MIT team found that the actomyosin network contains a great deal of redundancy — that is, most of the “nodes” of the network are connected to many other nodes.</p> <p>This built-in redundancy is analogous to a good public transit system, where if one bus or train line goes down, you can still get to your destination. Because cells can generate mechanical tension along many different pathways, they can fold the right way even if many of the cells in the network are damaged.</p> <p>“If you and I are holding a single rope, and then we cut it in the middle, it would come apart. But if you have a net, and cut it in some places, it still stays globally connected and can transmit forces, as long as you don’t cut all of it,” Dunkel says.</p> <p><strong>Folding framework</strong></p> <p>The researchers also found that the connections between cells preferentially organize themselves to run in the same direction as the furrow that forms in the early stages of folding.</p> <p>“We think this is setting up a frame around which the tissue will adopt its shape,” Martin says. “If you prevent the directionality of the connections, then what happens is you can still get folding but it will fold along the wrong axis.”</p> <p>Although this study was done in fruit flies, similar folding occurs in vertebrates (including humans) during the formation of the neural tube, which is the precursor to the brain and spinal cord. Martin now plans to apply the techniques he used in fruit flies to see if the actomyosin network is organized the same way in the neural tube of mice. Defects in the closure of the neural tube can lead to birth defects such as spina bifida.</p> <p>“We would like to understand how it goes wrong,” Martin says. “It’s still not clear whether it’s the sealing up of the tube that’s problematic or whether there are defects in the folding process.”</p> <p>The research was funded by the National Institute of General Medical Sciences and the James S. McDonnell Foundation.</p> At top left are the cell membranes of a normal fruit fly embryo, and at right, overlaid in pink, is the myosin network that connects the cells. At bottom left are the cell membranes of an embryo in which myosin is degraded, and at bottom right, the myosin network is outlined in pink. In both cases, the embryos can still fold normally.Credit: Hannah YevickResearch, Biology, Mathematics, School of Science, Space, astronomy and planetary science J-PAL North America announces second round of competition partners Education, Technology, and Opportunity Innovation Competition aims to improve student learning. Wed, 17 Jul 2019 09:25:01 -0400 J-PAL North America <p><a href="">J-PAL North America</a>, a research center at MIT, will partner with two leading education technology nonprofits to test promising models to improve learning, as part of the center’s second Education, Technology, and Opportunity Innovation Competition.&nbsp;</p> <p>Running in its second year, J-PAL North America’s Education, Technology, and Opportunity Innovation Competition supports education leaders in using randomized evaluations to generate evidence on&nbsp;how technology can improve student learning, particularly for students experiencing poverty or facing barriers to academic success. Last year, J-PAL North America partnered with the <a href="">Family Engagement Lab</a> to develop an evaluation of a multilingual digital messaging platform, and with <a href="">Western Governors University</a>’s Center for Applied Learning Science to evaluate scalable models to improve student learning in math.</p> <p>This year, J-PAL North America will continue its work to support rigorous evaluations of educational technologies aimed to reduce disparities by partnering with <a href="">Boys &amp; Girls Clubs of Greater Houston</a>, a youth-development organization that provides education and social services to students from low-income families, and <a href="">MIND Research Institute</a>, a nonprofit committed to improving math education.</p> <p>“Even just within the first and second year of the J-PAL ed-tech competition, there continues to be an explosion in promising new initiatives,” says <a href="">Philip Oreopoulos</a>, professor of economics at the University of Toronto and co-chair of the J-PAL <a href="">Education, Technology, and Opportunity Initiative</a>. “We’re excited to try to help steer this development towards the most promising and effective programs for improving academic success and student well-being.”</p> <p>Boys &amp; Girls Clubs of Greater Houston will partner with J-PAL North America to develop an evaluation of the <a href="">BookNook</a> reading app, a research-based intervention technology that aims to improve literacy skills of K-8 students.</p> <p>“One of our commitments to our youth is to prepare them to be better citizens in life, and we do this through our programming, which supplements the education they receive in school,” says Michael Ewing, director of programs at Boys &amp; Girls Clubs of Greater Houston. “BookNook is one of our programs that we know can increase reading literacy and help students achieve at a higher level. We are excited about this opportunity to conduct a rigorous evaluation of BookNook’s technology because we can substantially increase our own accountability as an organization, ensuring that we are able to track the literacy gains of our students when the program is implemented with fidelity.”</p> <p>Children who do not master reading by a young age are often <a href="">placed at a significant disadvantage to their peers</a> throughout the rest of their development. However, many <a href="">effective interventions for students struggling with reading</a> involve one-on-one or small-group instruction that places a heavy demand on school resources and teacher time. This makes it particularly challenging for schools that are already resource-strapped and face a shortage of teachers to meet the needs of students who are struggling with reading.</p> <p>The BookNook app offers a channel to bring research-proven literacy intervention strategies to greater numbers of students through accessible technology. The program is heavily scaffolded so that both teachers and non-teachers can use it effectively, allowing after-school staff like those at Boys &amp; Girls Clubs of Greater Houston to provide adaptive instruction to students struggling with reading.</p> <p>“Our main priority at BookNook is student success,” says Nate Strong, head of partnerships at for the BookNook team. “We are really excited to partner with J-PAL and with Boys &amp; Girls Clubs of Greater Houston to track the success of students in Houston and learn how we can do better for them over the long haul.”</p> <p>MIND Research Institute seeks to partner with J-PAL North America to develop a scalable model that will increase students’ conceptual understanding of mathematical concepts. <a href=";__hssc=142723050.1.1561132912684&amp;__hsfp=2177231255&amp;_ga=2.24602942.588039468.1561132912-972992093.1559936738">MIND’s Spatial Temporal (ST) math program</a> is a pre-K-8 visual instructional program that leverages the brain's spatial-temporal reasoning ability using challenging visual puzzles, non-routine problem solving, and animated informative feedback to understand and solve mathematical problems.</p> <p>“We’re thrilled and honored to begin this partnership with J-PAL to build our capacity to conduct randomized evaluations,” says Andrew Coulson, chief data science officer for MIND. “It's vital we continue to rigorously evaluate the ability of ST Math's spatial-temporal approach to provide a level playing field for every student, and to show substantial effects on any assessment. With the combination of talent and experience that J-PAL brings, I expect that we will also be exploring innovative research questions, metrics and outcomes, methods and techniques to improve the applicability, validity and real-world usability of the findings.”</p> <p>J-PAL North America is excited to work with these two organizations and continue to support rigorous evaluations that will help us better understand the role technology should play in learning. Boys &amp; Girls Clubs of Greater Houston and MIND Research Institute will help J-PAL contribute to growing evidence base on education technology that can help guide decision-makers in understanding which uses of education technology are truly helping students learn amidst a rapidly-changing technological landscape.</p> <p>J-PAL North America is a regional office of the Abdul Latif Jameel Poverty Action Lab. J-PAL was established in 2003 as a research center at MIT’s Department of Economics. Since then, it has built a global network of affiliated professors based at over 58 universities and regional offices in Africa, Europe, Latin America and the Caribbean, North America, South Asia, and Southeast Asia. J-PAL North America was established with support from the Alfred P. Sloan Foundation and Arnold Ventures and works to improve the effectiveness of social programs in North America through three core activities: research, policy outreach, and capacity building. J-PAL North America’s education technology work is supported by the Overdeck Family Foundation and Arnold Ventures.</p> J-PAL North America’s Education, Technology, and Opportunity Innovation Competition supports education leaders in using randomized evaluations to generate evidence on how technology can improve student learning, particularly for students from disadvantaged backgrounds.Abdul Latif Jameel Poverty Action Lab (J-PAL), Economics, School of Humanities Arts and Social Sciences, Learning, K-12 education, Technology and society, STEM education, Mathematics, Education, teaching, academics Meet the 2019 tenured professors in the School of Science Eight faculty members are granted tenure in five science departments. Wed, 10 Jul 2019 11:20:01 -0400 School of Science <p>MIT granted tenure to eight School of Science faculty members in the departments of Biology; Chemistry; Earth, Atmospheric and Planetary Sciences; Mathematics; and Physics.</p> <p><a href="">William Detmold</a>’s research within the area of theoretical particle and nuclear physics incorporates analytical methods, as well as the power of the world’s largest supercomputers, to understand the structure, dynamics, and interactions of particles like protons and to look for evidence of new physical laws at the sub-femtometer scale probed in experiments such as those at the Large Hadron Collider. He joined the Department of Physics in 2012 from the College of William and Mary, where he was an assistant professor. Prior to that, he was a research assistant professor at the University of Washington. He received his BS and PhD from the University of Adelaide in Australia in 1996 and 2002, respectively. Detmold is a researcher in the Center for Theoretical Physics in the Laboratory for Nuclear Science.<br /> <br /> <a href="">Semyon Dyatlov</a> explores scattering theory, quantum chaos, and general relativity by employing microlocal analytical and dynamical system methods. He came to the Department of Mathematics as a research fellow in 2013 and became an assistant professor in 2015. He completed his doctorate in mathematics at the University of California at Berkeley in 2013 after receiving a BS in mathematics at Novosibirsk State University in Russia in 2008. Dyatlov spent time after finishing his PhD as a postdoc at the Mathematical Sciences Research Institute before moving to MIT.</p> <p><a href="">Mary Gehring</a> studies plant epigenetics. By using a combination of genetic, genomic, and molecular biology, she explores how plants inherit and interpret information that is not encoded in their DNA to better understand plant growth and development. Her lab focuses primarily on <em>Arabidopsis thaliana</em>, a small flowering plant that is a model species for plant research. Gehring joined the Department of Biology in 2010 after performing postdoctoral research at the Fred Hutchinson Cancer Research Center. She received her BA in biology from Williams College in 1998 and her doctorate from the University of California at Berkeley in 2005. She is also a member of the Whitehead Institute for Biomedical Research.</p> <p><a href="">David</a><a href=""> McGee</a> performs research in the field of paleoclimate, merging information from stalagmites, lake deposits, and marine sediments with insights from models and theory to understand how precipitation patterns and atmospheric circulation varied in the past. He came to MIT in 2012, joining the Department of Earth, Atmospheric and Planetary Sciences after completing a NOAA Climate and Global Change Postdoctoral Fellowship at the University of Minnesota. Before that, he attended Carleton College for his BA in geology in 1993-97, Chatham College for an MA in teaching from 1999 to 2003, Tulane University for his MS from 2004 to 2006, and Columbia University for his PhD from 2006 to 2009. McGee is the director of the MIT Terrascope First-Year Learning Community, a role he has held for the past four years.</p> <p><a href="">Ankur Moitra</a> works at the interface between theoretical computer science and machine learning by developing algorithms with provable guarantees and foundations for reasoning about their behavior. He joined the Department of Mathematics in 2013. Prior to that, he received his BS in electrical and computer engineering from Cornell University in 2007, and his MS and PhD in computer science from MIT in 2009 and 2011, respectively. He was a National Science Foundation postdoc at the Institute for Advanced Study until 2013. Moitra was a 2018 recipient of a School of Science Teaching Prize. He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a core member of the Statistics and Data Science Center.</p> <p><a href="">Matthew Shoulders</a> focuses on integrating biology and chemistry to understand how proteins function in the cellular setting, including proteins’ shape, quantity, and location within the body. This research area has important implications for genetic disorders and neurodegenerative diseases such as Alzheimer’s, diabetes, cancer, and viral infections. Shoulders’ lab works to elucidate, at the molecular level, how cells solve the protein-folding problem, and then uses that information to identify how diseases can develop and to provide insight into new targets for drug development. Shoulders joined the Department of Chemistry in 2012 after earning a BS in chemistry and minor in biochemistry from Virginia Tech in 2004 and a PhD in chemistry from the University of Wisconsin at Madison in 2009. He is also an associate member of the Broad Institute of MIT and Harvard, and a member of the MIT Center for Environmental Health Sciences.</p> <p><a href="">Tracy Slatyer</a> researches fundamental aspects of theoretical physics, answering questions about both visible and dark matter by searching for potential indications of new physics in astrophysical and cosmological data. She has developed and adapted novel techniques for data analysis, modeling, and calculations in quantum field theory; her work has also inspired a range of experimental investigations. The Department of Physics welcomed Slatyer in 2013 after she completed a three-year postdoctoral fellowship at the Institute for Advanced Study. She majored in theoretical physics as an undergraduate at the Australian National University, receiving a BS in 2005, and completed her PhD in physics at Harvard University in 2010. In 2017, Slatyer received the School of Science Prize in Graduate Teaching and was also named the first recipient of the school’s Future of Science Award. She is a member of the Center for Theoretical Physics in the Laboratory for Nuclear Science.</p> <p><a href="">Michael Williams</a> uses novel experimental methods to improve our knowledge of fundamental particles, including searching for new particles and forces, such as dark matter. He also works on advancing the usage of machine learning within the domain of particle physics research. He joined the Department of Physics in 2012. He previously attended Saint Vincent College as an undergraduate, where he double majored in mathematics and physics. Graduating in 2001, Williams then pursued a doctorate at Carnegie Mellon University, which he completed in 2007. From 2008 to 2012 he was a postdoc at Imperial College London. He is a member of the Laboratory for Nuclear Science.</p> Clockwise from top left: William Detmold, Semyon Dyatlov, Mary Gehring, David McGee, Ankur Moitra, Matthew Shoulders, Tracy Slatyer, and Michael Williams.Photos courtesy of the faculty.School of Science, Biology, Chemistry, EAPS, Mathematics, Physics, Laboratory for Nuclear Science, Computer Science and Artificial Intelligence Laboratory (CSAIL), Broad Institute, Center for Environmental Health Sciences (CEHS), Faculty, Awards, honors and fellowships, Whitehead Institute, Center for Theoretical Physics Breaching a “carbon threshold” could lead to mass extinction Carbon dioxide emissions may trigger a reflex in the carbon cycle, with devastating consequences, study finds. Mon, 08 Jul 2019 14:59:59 -0400 Jennifer Chu | MIT News Office <p>In the brain, when neurons fire off electrical signals to their neighbors, this happens through an “all-or-none” response. The signal only happens once conditions in the cell breach a certain threshold.</p> <p>Now an MIT researcher has observed a similar phenomenon in a completely different system: Earth’s carbon cycle.</p> <p>Daniel Rothman, professor of geophysics and co-director of the Lorenz Center in MIT’s Department of Earth, Atmospheric and Planetary Sciences, has found that when the rate at which carbon dioxide enters the oceans pushes past a certain threshold — whether as the result of a sudden burst or a slow, steady influx — the Earth may respond with a runaway cascade of chemical feedbacks, leading to extreme ocean acidification that dramatically amplifies the effects of the original trigger.</p> <p>This global reflex causes huge changes in the amount of carbon contained in the Earth’s oceans, and geologists can see evidence of these changes in layers of sediments preserved over hundreds of millions of years.</p> <p>Rothman looked through these geologic records and observed that over the last 540 million years, the ocean’s store of carbon changed abruptly, then recovered, dozens of times in a fashion similar to the abrupt nature of a neuron spike. This “excitation” of the carbon cycle occurred most dramatically near the time of four of the five great mass extinctions in Earth’s history.</p> <p>Scientists have attributed various triggers to these events, and they have assumed that the changes in ocean carbon that followed were proportional to the initial trigger — for instance, the smaller the trigger, the smaller the environmental fallout.</p> <p>But Rothman says that’s not the case. It didn’t matter what initially caused the events; for roughly half the disruptions in his database, once they were set in motion, the rate at which carbon increased was essentially the same.&nbsp; Their characteristic rate is likely a property of the carbon cycle itself — not the triggers, because different triggers would operate at different rates.</p> <p>What does this all have to do with our modern-day climate? Today’s oceans are absorbing carbon about an order of magnitude faster than the worst case in the geologic record — the end-Permian extinction. But humans have only been pumping carbon dioxide into the atmosphere for hundreds of years, versus the tens of thousands of years or more that it took for volcanic eruptions or other disturbances to trigger the great environmental disruptions of the past. Might the modern increase of carbon be too brief to excite a major disruption?</p> <p>According to Rothman, today we are “at the precipice of excitation,” and if it occurs, the resulting spike — as evidenced through ocean acidification, species die-offs, and more — is likely to be similar to past global catastrophes.</p> <p>“Once we’re over the threshold, how we got there may not matter,” says Rothman, who is publishing his results this week in the <em>Proceedings of the National Academy of Sciences.</em> “Once you get over it, you’re dealing with how the Earth works, and it goes on its own ride.”</p> <p><strong>A carbon feedback</strong></p> <p>In 2017, Rothman made <a href="">a dire prediction</a>: By the end of this century, the planet is likely to reach a critical threshold, based on the rapid rate at which humans are adding carbon dioxide to the atmosphere. When we cross that threshold, we are likely to set in motion a freight train of consequences, potentially culminating in the Earth’s sixth mass extinction.</p> <p>Rothman has since sought to better understand this prediction, and more generally, the way in which the carbon cycle responds once it’s pushed past a critical threshold. In the new paper, he has developed a simple mathematical model to represent the carbon cycle in the Earth’s upper ocean and how it might behave when this threshold is crossed.</p> <p>Scientists know that when carbon dioxide from the atmosphere dissolves in seawater, it not only makes the oceans more acidic, but it also decreases the concentration of carbonate ions. When the carbonate ion concentration falls below a threshold, shells made of calcium carbonate dissolve. Organisms that make them fare poorly in such harsh conditions.</p> <p>Shells, in addition to protecting marine life, provide a “ballast effect,” weighing organisms down and enabling them to sink to the ocean floor along with detrital organic carbon, effectively removing carbon dioxide from the upper ocean. But in a world of increasing carbon dioxide, fewer calcifying organisms should mean less carbon dioxide is removed.</p> <p>“It’s a positive feedback,” Rothman says. “More carbon dioxide leads to more carbon dioxide. The question from a mathematical point of view is, is such a feedback enough to render the system unstable?”</p> <p><strong>“</strong><strong>An inexorable rise</strong><strong>”</strong></p> <p>Rothman captured this positive feedback in his new model, which comprises two differential equations that describe interactions between the various chemical constituents in the upper ocean. He then observed how the model responded as he pumped additional carbon dioxide into the system, at different rates and amounts.</p> <p>He found that no matter the rate at which he added carbon dioxide to an already stable system, the carbon cycle in the upper ocean remained stable. In response to modest perturbations, the carbon cycle would go temporarily out of whack and experience a brief period of mild ocean acidification, but it would always return to its original state rather than oscillating into a new equilibrium.</p> <p>When he introduced carbon dioxide at greater rates, he found that once the levels crossed a critical threshold, the carbon cycle reacted with a cascade of positive feedbacks that magnified the original trigger, causing the entire system to spike, in the form of severe ocean acidification. The system did, eventually, return to equilibrium, after tens of thousands of years in today’s oceans — an indication that, despite a violent reaction, the carbon cycle will resume its steady state.</p> <p>This pattern matches the geological record, Rothman found. The characteristic rate exhibited by half his database results from excitations above, but near, the threshold. Environmental disruptions associated with mass extinction are outliers — they represent excitations well beyond the threshold. At least three of those cases may be related to sustained massive volcanism.</p> <p>“When you go past a threshold, you get a free kick from the system responding by itself,” Rothman explains. “The system is on an inexorable rise. This is what excitability is, and how a neuron works too.”</p> <p>Although carbon is entering the oceans today at an unprecedented rate, it is doing so over a geologically brief time. Rothman’s model predicts that the two effects cancel: Faster rates bring us closer to the threshold, but shorter durations move us away. Insofar as the threshold is concerned, the modern world is in roughly the same place it was during longer periods of massive volcanism.&nbsp;</p> <p>In other words, if today’s human-induced emissions cross the threshold and continue beyond it, as Rothman predicts they soon will, the consequences may be just as severe as what the Earth experienced during its previous mass extinctions.</p> <p>“It’s difficult to know how things will end up given what’s happening today,” Rothman says. “But we’re probably close to a critical threshold. Any spike would reach its maximum after about 10,000 years. Hopefully that would give us time to find a solution.”</p> <p>“We already know that our CO<sub>2</sub>-emitting actions will have consequences for many millennia,” says Timothy Lenton, professor of climate change and earth systems science at the University of Exeter. “This study suggests those consequences could be much more dramatic than previously expected. If we push the Earth system too far, then it takes over and determines its own response — past that point there will be little we can do about it.”</p> <p>This research was supported, in part, by NASA and the National Science Foundation.</p> When carbon emissions pass a critical threshold, it can trigger a spike-like reflex in the carbon cycle, in the form of severe ocean acidification that lasts for 10,000 years, according to a new MIT study.Stock imageCarbon, Climate, Climate change, EAPS, Earth and atmospheric science, Emissions, Environment, Geology, Global Warming, Greenhouse gases, Mathematics, Research, School of Science, NASA, National Science Foundation (NSF) Seven MIT educators honored for digital learning innovation Educators recognized for improving classroom instruction and student engagement through innovative uses of digital technology. Tue, 02 Jul 2019 13:30:00 -0400 Kelly McSweeney | MIT Open Learning <p>Seven MIT educators have received awards this year for their significant digital learning innovations and their contributions to teaching and learning at MIT and around the world.</p> <p>Polina Anikeeva, Martin Bazant, and Jessica Sandland shared the third annual <em>MITx</em> Prize for Teaching and Learning in MOOCs —&nbsp;an award given to educators who have developed massive open online courses (MOOCs) that share the best of MIT knowledge and perspectives with learners around the world. Additionally, John Belcher, Amy Carleton, Jared Curhan, and Erik Demaine received Teaching with Digital Technology Awards, nominated by MIT students for their innovative use of digital technology to improve their teaching at MIT.</p> <p><strong>The <em>MITx</em> Prize for Teaching and Learning in MOOCs</strong></p> <p>This year’s <em>MITx </em>prize winners were honored at an MIT Open Learning event in May. Professor Polina Anikeeva of the Department of Materials Science and Engineering and Digital Learning Lab Scientist Jessica Sandland received the award for teaching 3.024x (Electronic, Optical and Magnetic Properties of Materials). The course was praised for not only its global impact, but also for the way in which it enhanced the residential experience. Increased flexibility from integrating the online content allowed for the addition of design reviews, which give MIT students firsthand experience working on complicated engineering problems.</p> <p>3.024x is fast-paced and challenging. To bring some levity to the subject, the instructors designed problem sets around a series of superhero-themed comic strips that integrated the science and engineering concepts that students learned in class.</p> <p>Martin Bazant, of the departments of Chemical Engineering and Mathematics, received the <em>MITx </em>prize for his course, 10.50.1x (Analysis of Transport Phenomena Mathematical Methods). Most problems in the course involve long calculations, which can be tricky to demonstrate online.</p> <p>To solve this challenge, Bazant broke up problems into smaller parts that included tips and tutorials to help learners solve the problem while maintaining the rigorous intellectual challenge. Course participants included a diverse group of college students, industry professionals, and faculty from other universities in many science and engineering disciplines across the globe.</p> <p><strong>Teaching with Digital Technology Awards</strong></p> <div class="cms-placeholder-content-video"></div> <p>Co-sponsored by MIT Open Learning and the Office of the Vice Chancellor, the Teaching with Digital Technology Awards are student-nominated awards for faculty and instructors who have improved teaching and learning at MIT with digital technology. MIT students nominated 117 faculty and instructors for this award this year, more than in any previous year. The winners were celebrated at an awards luncheon in early June. John Belcher, Erik Demaine, and Jared Curhan attended the awards luncheon, and — in the spirit of an award reception for digital innovation — Amy Carleton joined the event virtually, through video chat.</p> <p>John Belcher was honored for his physics courses on electricity and magnetism. Students appreciated the way that Belcher incorporated videos with his lectures to help provide a physical representation of an abstract subject. He created the animated videos to show visualizations of fundamental physics concepts such as energy transfer and magnetic fields. Students remarked that the videos helped them learn about everything from solar flares and the solar cycle to the fundamentally relativistic nature of electromagnetism.</p> <p>Erik Demaine of the Computer Science and Artificial Intelligence Lab received the award for his course 6.892 (Fun with Hardness Proofs). The course flipped the traditional classroom model. Instead of lecturing in person, all lectures were posted online and problems were done in class. This allowed the students to spend class time working together on collaborative problem solving through an online application that Demaine created, called Coauthor.</p> <p>Jared Curhan received the award for his negotiation courses at the MIT Sloan School of Management, including 15.672 (Negotiation Analysis), which he designed for students across the Institute. Curhan used digital technology to provide feedback while students practiced their negotiating skills in class. A platform called iDecisionGames helped simulate negotiation exercises between students, and after each exercise it provided data about how each participant performed, both objectively and subjectively.</p> <p>Amy Carleton received the award for her course on science writing and new media. During the course, students learned how to write about scientific and technical topics for a general audience. They put their skills to work by writing Wikipedia articles, where they used advanced editing techniques and wrote mathematical expressions in LaTEX. They also used Google Docs during class to edit articles in small groups, and developed PowerPoint presentations where they learned to incorporate sound and graphics to emphasize their ideas.</p> <p>Both awards celebrate instructors who are using technology in innovative ways to help teach challenging courses to both traditional students and online learners.</p> <p>“At MIT, there is no shortage of digital learning innovation, and this year’s winners reflect the Institute’s strong commitment to transforming teaching and learning at MIT and around the globe,” says MIT Professor Krishna Rajagopal, dean for digital learning. “They have set new standards for online and blended learning.”</p> Dean of Digital Learning Krishna Rajagopal (center) with winners of the MITx Prize for Teaching and Learning in MOOCs, Jessica Sandland (left) and Martin Bazant. Not pictured: Polina Anikeeva, who also received the MITx Prize, and John Belcher, Amy Carleton, Jared Curhan, and Erik Demaine, who received Teaching with Digital Technology Awards.Photo: MIT Open LearningMITx, Office of Open Learning, Materials Science and Engineering, Chemical engineering, Mathematics, School of Engineering, School of Science, Office of the Vice Chancellor, Physics, Computer Science and Artificial Intelligence Laboratory (CSAIL), Sloan School of Management, online learning, Massive open online courses (MOOCs), OpenCourseWare, Classes and programs, Technology and society, Education, teaching, academics, Awards, honors and fellowships MIT Energy Initiative awards seven Seed Fund grants for early-stage energy research Annual MITEI awards support research on methane conversion, efficient energy provision, plastics recycling, and more. Mon, 01 Jul 2019 13:05:01 -0400 MIT Energy Initiative <p>The MIT Energy Initiative (MITEI) recently awarded seven grants totaling approximately $1 million through its <a href="" target="_blank">Seed Fund Program</a>, which supports early-stage innovative energy research at MIT through an annual competitive process.</p> <p>“Supporting basic research has always been a core component of MITEI’s mission to transform and decarbonize global energy systems,” says MITEI Director <a href="" target="_blank">Robert C. Armstrong</a>, the Chevron Professor of Chemical Engineering. “This year’s funded projects highlight just a few examples of the many ways that people working across the energy field are researching vital topics to create a better world.”</p> <p>The newly awarded projects will address topics such as developing efficient strategies for recycling plastics, improving the stability of high-energy metal-halogen flow batteries, and increasing the potential efficiency of silicon solar cells to accelerate the adoption of photovoltaics. Awardees include established energy faculty members and others who are new to the energy field, from disciplines including applied economics, chemical engineering, biology, and other areas.</p> <p><strong>Demand-response policies and incentives for energy efficiency adoption</strong></p> <p>Most of today’s energy growth is occurring in developing countries. Assistant Professor <a href="" target="_blank">Namrata Kala</a> and Professor <a href="" target="_blank">Christopher Knittel</a>, both of whom focus on applied economics at the MIT Sloan School of Management, will use their grant to examine key policy levers for meeting electricity demand and renewable energy growth without jeopardizing system reliability in the developing world.</p> <p>Kala and Knittel plan to design and run a randomized control trial in New Delhi, India, in collaboration with a large Indian power company. “We will estimate the willingness of firms to enroll in services that reduce peak consumption, and also promote energy efficiency,” says Kala, the W. Maurice Young (1961) Career Development Professor of Management. “Estimating the costs and benefits of such services, and their allocation across customers and electricity providers, can inform policies that promote energy efficiency in a cost-effective manner.”</p> <p><strong>Efficient conversion of methane to methanol&nbsp; </strong></p> <p>Methane, the primary component of natural gas, has become an increasingly important part of the global energy portfolio. However, the chemical inertness of methane and the lack of efficient methods to convert this gaseous carbon feedstock into liquid fuels has significantly limited its application. <a href="">Yang Shao-Horn</a>,&nbsp;the W.M. Keck Professor of Energy in the departments of Mechanical Engineering and Materials Science and Engineering, seeks to address this problem using her seed fund grant. Shao-Horn and Shuai Yuan, a postdoc in the Research Laboratory of Electronics, will focus on achieving efficient, cost-effective gas-to-liquid conversion using metal-organic frameworks (MOFs) as electrocatalysts.</p> <p>Current methane activation and conversion processes are usually accomplished by costly and energy-intensive steam reforming at elevated temperature and high pressure. Shao-Horn and Yuan’s goal is to design efficient MOF-based electrocatalysts that will permit the methane-to-methanol conversion process to proceed at ambient temperature and pressure.</p> <p>“If successful, this electrochemical gas-to-liquid concept could lead to a modular, efficient, and cost-effective solution that can be deployed in both large-scale industrial plants and remotely located oil fields to increase the utility of geographically isolated gas reserves,” says Shao-Horn.</p> <p><strong>Using&nbsp;machine learning to solve the “zeolite conundrum”</strong><br /> <br /> The energy field is replete with opportunities for machine learning to expedite progress toward a variety of innovative energy solutions. <a href="">Rafael Gómez-Bombarelli</a>, the Toyota Assistant Professor in Materials Processing in the Department of Materials Science and Engineering, received a grant for a project that will combine machine learning and simulation to accelerate the discovery cycle of zeolites.<br /> &nbsp;<br /> Zeolites are materials with wide-ranging industrial applications as catalysts and molecular sieves because of their high stability and selective nanopores that can confine small molecules. Despite decades of abundant research, only 248 zeolite frameworks have been realized out of the millions of possible structures that have been proposed using computers — the so-called zeolite conundrum.<br /> &nbsp;<br /> The problem, notes Gómez-Bombarelli, is that discovery of these new frameworks has relied mostly on trial-and-error in the lab — an approach that is both slow and labor-intensive.<br /> &nbsp;<br /> In his seed grant work, Gómez-Bombarelli and his team will be using theory to speed up that process. “Using machine learning and first-principles simulations, we’ll design small molecules to dock on specific pores and direct the formation of targeted structures,” says Gómez-Bombarelli. “This computational approach will drive new synthetic outcomes in zeolites faster.”</p> <p><strong>Effective recycling of plastics</strong></p> <p>Professor <a href="">Anthony Sinskey</a> of the Department of Biology, Professor <a href="">Gregory Stephanopoulos</a> of the Department of Chemical Engineering,&nbsp;and graduate student Linda Zhong of biology have joined forces to address the environmental and economic problems posed by polyethylene terephthalate (PET). One of the most synthesized plastics, PET exhibits an extremely low degradation rate and its production is highly dependent on petroleum feedstocks.</p> <p>“Due to the huge negative impacts of PET products, efficient recycling strategies need to be designed to decrease economic loss and adverse environmental impacts associated with single-use practices,” says Sinskey.</p> <p>“PET is essentially an organic polymer of terephthalic acid and ethylene glycol, both of which can be metabolized by bacteria as energy and nutrients. These capacities exist in nature, though not together,” says Zhong. “Our goal is to engineer these metabolic pathways into<em> E. coli</em> to allow the bacterium to grow on PET. Using genetic engineering, we will introduce the PET-degrading enzymes into <em>E. coli </em>and ultimately transfer them into bioremediation organisms.”</p> <p>The long-term goal of the project is to prototype a bioprocess for closed-loop PET recycling, which will decrease the volume of discarded PET products as well as the consumption of petroleum and energy for PET synthesis.</p> <p>The researchers’ primary motivation in pursuing this project echoes MITEI’s overarching goal for the seed fund program: to push the boundaries of research and innovation to solve global energy and climate challenges. Zhong says, “We see a dire need for this research because our world is inundated in plastic trash. We’re only attempting to solve a tiny piece of the global problem, but we must try when much of what we hold dear depends on it.”</p> <p>The MITEI Seed Fund Program has awarded new grants each year since it was established in 2008. Funding for the grants comes chiefly from MITEI’s founding and sustaining members, supplemented by gifts from generous donors. To date, MITEI has supported 177 projects with grants totaling approximately $23.6 million.</p> <p>Recipients of MITEI Seed Fund grants for 2019 are:</p> <ul> <li>"Development and prototyping of stable, safe, metal‐halogen flow batteries with high energy and power densities"<strong>&nbsp;</strong>—&nbsp;Martin Bazant of the departments of Chemical Engineering and Mathematics and T. Alan Hatton of the Department of Chemical Engineering;<br /> &nbsp;</li> <li>"Silicon solar cells sensitized by exciton fission" —&nbsp;Marc Baldo of the Department of Electrical Engineering and Computer Science;<br /> &nbsp;</li> <li>"Automatic design of structure‐directing agents for novel realizable zeolites" —&nbsp;Rafael Gómez‐Bombarelli of the Department of Materials Science and Engineering;<br /> &nbsp;</li> <li>"Demand response, energy efficiency, and firm decisions" —&nbsp;Namrata Kala and Christopher Knittel of the Sloan School of Management;<br /> &nbsp;</li> <li>"Direct conversion of methane to methanol by MOF‐based electrocatalysts"<strong>&nbsp;</strong>—&nbsp;Yang Shao‐Horn of the departments of Mechanical Engineering and Materials Science and Engineering;<br /> &nbsp;</li> <li>"Biodegradation of plastics for efficient recycling and bioremediation" —&nbsp;Anthony Sinskey of the Department of Biology and&nbsp;Gregory Stephanopoulos of the Department of Chemical Engineering; and<br /> &nbsp;</li> <li>"Asymmetric chemical doping for photocatalytic CO2 reduction" —&nbsp;Michael Strano of the Department of Chemical Engineering.</li> </ul> One of the most synthesized plastics, PET — found in plastic bottles — exhibits an extremely low degradation rate and its production is highly dependent on petroleum feedstocks. New research on the biodegradation of plastics will be funded by a MIT Energy Initiative Seed Fund. Photo: seefromthesky/UnsplashMIT Energy Initiative (MITEI), Sloan School of Management, Mechanical engineering, Materials Science and Engineering, School of Engineering, Research Laboratory of Electronics, Biology, Chemical engineering, School of Science, Mathematics, Electrical Engineering & Computer Science (eecs), Funding, Grants, Faculty, Campaign for a Better World QS ranks MIT the world’s No. 1 university for 2019-20 Ranked at the top for the eighth straight year, the Institute also places first in 11 of 48 disciplines. Tue, 18 Jun 2019 20:01:00 -0400 MIT News Office <p>MIT has again been named the world’s top university by the QS World University Rankings, which were announced today. This is the eighth year in a row MIT has received this distinction.</p> <p>The full 2019-20 rankings — published by Quacquarelli Symonds, an organization specializing in education and study abroad — can be found at <a href=""></a>. The QS rankings were based on academic reputation, employer reputation, citations per faculty, student-to-faculty ratio, proportion of international faculty, and proportion of international students. MIT earned a perfect overall score of 100.</p> <p>MIT was also ranked the world’s top university in <a href="">11 of 48 disciplines ranked by QS</a>, as announced in February of this year.</p> <p>MIT received a No. 1 ranking in the following QS subject areas: Chemistry; Computer Science and Information Systems; Chemical Engineering; Civil and Structural Engineering; Electrical and Electronic Engineering; Mechanical, Aeronautical and Manufacturing Engineering; Linguistics; Materials Science; Mathematics; Physics and Astronomy; and Statistics and Operational Research.</p> <p>MIT also placed second in six subject areas: Accounting and Finance; Architecture/Built Environment; Biological Sciences; Earth and Marine Sciences; Economics and Econometrics; and Environmental Sciences.</p> Image: Christopher HartingRankings, Architecture, Chemical engineering, Chemistry, Civil and environmental engineering, Electrical Engineering & Computer Science (eecs), Economics, Linguistics, Materials Science and Engineering, DMSE, Mechanical engineering, Aeronautical and astronautical engineering, Physics, Business and management, Accounting, Finance, Arts, Design, Mathematics, EAPS, School of Architecture and Planning, School of Humanities Arts and Social Sciences, School of Science, School of Engineering, Sloan School of Management A droplet walks into an electric field … Researchers have found a simple formula that could be useful for air purification, space propulsion, and molecular analyses. Mon, 17 Jun 2019 00:00:00 -0400 Jennifer Chu | MIT News Office <p>When a raindrop falls through a thundercloud, it is subject to strong electric fields that pull and tug on the droplet, like a soap bubble in the wind. If the electric field is strong enough, it can cause the droplet to burst apart, creating a fine, electrified mist.</p> <p>Scientists began taking notice of how droplets behave in electric fields in the early 1900s, amid concerns over lightning strikes that were damaging newly erected power lines. They soon realized that the power lines’ own electric fields were causing raindrops to burst around them, providing a conductive path for lightning to strike. This revelation led engineers to design thicker coverings around power lines to limit lightning strikes.</p> <p>Today, scientists understand that the stronger the electric field, the more likely it is that a droplet within it will burst. But, calculating the exact field strength that will burst a particular droplet has always been an involved mathematical task.</p> <p>Now, MIT researchers have found that the conditions for which a droplet bursts in an electric field all boil down to one simple formula, which the team has derived for the first time.</p> <p>With this simple new equation, the researchers can predict the exact strength an electric field should be to burst a droplet or keep it stable. The formula applies to three cases previously analyzed separately: a droplet pinned on a surface, sliding on a surface, or free-floating in the air.</p> <p>Their results, published today in the journal <em>Physical Review Letters</em>, may help engineers tune the electric field or the size of droplets for a range of applications that depend on electrifying droplets. These include &nbsp;technologies for air or water purification, space propulsion, and molecular analysis.</p> <p>“Before our result, engineers and scientists had to perform computationally intensive simulations to assess the stability of an electrified droplet,” says lead author Justin Beroz, a graduate student in MIT’s departments of Mechanical Engineering and Physics. “With our equation, one can predict this behavior immediately, with a simple paper-and-pencil calculation. This is of great practical benefit to engineers working with, or trying to design, any system that involves liquids and electricity.”</p> <p>Beroz’ co-authors are A. John Hart, associate professor of mechanical engineering, and John Bush, professor of mathematics.</p> <p><strong>“Something unexpectedly simple”</strong></p> <p>Droplets tend to form as perfect little spheres due to surface tension, the cohesive force that binds water molecules at a droplet’s surface and pulls the molecules inward. The droplet may distort from its spherical shape in the presence of other forces, such as the force from an electric field. While surface tension acts to hold a droplet together, the electric field acts as an opposing force, pulling outward on the droplet as charge builds on its surface.</p> <p>“At some point, if the electric field is strong enough, the droplet can’t find a shape that balances the electrical force, and at that point, it becomes unstable and bursts,” Beroz explains.</p> <p>He and his team were interested in the moment just before bursting, when the droplet has been distorted to its critically stable shape. The team set up an experiment in which they slowly dispensed water droplets onto a metal plate that was electrified to produce an electric field, and used a high-speed camera to record the distorted shapes of each droplet.</p> <p>“The experiment is really boring at first — you’re watching the droplet slowly change shape, and then all of a sudden it just bursts,” Beroz says.</p> <p>After experimenting on droplets of different sizes and under various electric field strengths, Beroz isolated the video frame just before each droplet burst, then outlined its critically stable shape and calculated several parameters such as the droplet’s volume, height, and radius. He plotted the data from each droplet and found, to his surprise, that they all fell along an unmistakably straight line.</p> <p>“From a theoretical point of view, it was an unexpectedly simple result given the mathematical complexity of the problem,” Beroz says. “It suggested that there might be an overlooked, yet simple, way to calculate the burst criterion for the droplets.”</p> <p><img alt="" src="/sites/" style="width: 480px; height: 278px;" /></p> <p><em><span style="font-size:10px;">A water droplet, subject to an electric field of slowly increasing strength, suddenly bursts by emitting a fine, electrified mist from its apex.</span></em></p> <p><strong>Volume above height</strong></p> <p>Physicists have long known that a liquid droplet in an electric field can be represented by a set of coupled nonlinear differential equations. These equations, however, are incredibly difficult to solve. To find a solution requires determining the configuration of the electric field, the shape of the droplet, and the pressure inside the droplet, simultaneously.</p> <p>“This is commonly the case in physics: It’s easy to write down the governing equations but very hard to actually solve them,” Beroz says. “But for the droplets, it turns out that if you choose a particular combination of physical parameters to define the problem from the start, a solution can be derived in a few lines. Otherwise, it’s impossible.”</p> <p>Physicists who attempted to solve these equations in the past did so by factoring in, among other parameters, a droplet’s height — an easy and natural choice for characterizing a droplet’s shape. But Beroz made a different choice, reframing the equations in terms of a droplet’s volume rather than its height. This was the key insight for reformulating the problem into an easy-to-solve formula.</p> <p>“For the last 100 years, the convention was to choose height,” Beroz says. “But as a droplet deforms, its height changes, and therefore the mathematical complexity of the problem is inherent in the height. On the other hand, a droplet’s volume remains fixed regardless of how it deforms in the electric field.”</p> <p>By formulating the equations using only parameters that are “fixed” in the same sense as a droplet’s volume, “the complicated, unsolvable parts of the equation cancel out, leaving a simple equation that matches the experimental results,” Beroz says.</p> <p>Specifically, the new formula the team derived relates five parameters: a droplet’s surface tension, radius, volume, electric field strength, and the electric permittivity of the air surrounding the droplet. Plugging any four of these parameters into the formula will calculate the fifth.</p> <p>Beroz says engineers can use the formula to develop techniques such as electrospraying, which involves the bursting of a droplet maintained at the orifice of an electrified nozzle to produce a fine spray. Electrospraying is commonly used to aerosolize biomolecules from a solution, so that they can pass through a spectrometer for detailed analysis. The technique is also used to produce thrust and propel satellites in space.</p> <p>“If you’re designing a system that involves liquids and electricity, it’s very practical to have an equation like this, that you can use every day,” Beroz says.</p> <p>This research was funded in part by the MIT Deshpande Center for Technological Innovation, BAE Systems, the Assistant Secretary of Defense for Research and Engineering via MIT Lincoln Laboratory, the National Science Foundation, and a Department of Defense National Defence Science and Engineering Graduate Fellowship.</p> Electrified water droplets take on a variety of distorted shapes just before bursting, based on the strength of the electric field. The profiles of different distorted droplet shapes are shown, overlaid on an image of one particular distorted droplet for comparison.Courtesy of the researchersFluid dynamics, Mathematics, Mechanical engineering, Research, School of Engineering, School of Science, Water, Physics, National Science Foundation (NSF) A scholar and teacher re-examines moments in the history of STEM “I love teaching,” says PhD student Clare Kim. “It’s not that I’m just imparting knowledge, but I want [my students] to develop a critical way of thinking.” Thu, 13 Jun 2019 23:59:59 -0400 Daysia Tolentino | MIT News correspondent <p>When Clare Kim began her fall 2017 semester as the teaching assistant for 21H.S01, the inaugural “MIT and Slavery” course, she didn’t know she and her students would be creating a historical moment of their own at the Institute.</p> <p>Along with Craig Steven Wilder, the Barton L. Weller Professor of History, and Nora Murphy, an archivist for researcher services in the MIT Libraries, Kim helped a team of students use archival materials to examine the Institute’s ties to slavery and how that legacy has impacted the modern structure of scientific institutions. The findings that came to light through the class thrust Kim and her students onto a prominent stage. They spoke about their research in media interviews and at a standing-room-only <a href="">community forum</a>, and helped bring MIT into a national conversation about universities and the institution of slavery in the United States.</p> <p>For Kim, a PhD student in MIT’s Program in History, Anthropology, and Science, Technology, and Society (HASTS), it was especially rewarding to help the students to think critically about their own scientific work through a historical context. She enjoyed seeing how the course challenged conventional ideas that had been presented to them about their various fields of study.</p> <p>“I think people tend to think too much about history as a series of true facts where the narrative that gets constructed is stabilized. Conducting historical research is fun because you have a chance to re-examine evidence, examine archival materials, reinterpret some of what has already been written, and craft a new narrative as a result,” Kim says.</p> <p>This year, Kim was awarded the prestigious Goodwin Medal for her work as a TA for several MIT courses. The award recognizes graduate teaching assistants that have gone the extra mile in the classroom. Faculty, colleagues, and former students praised Kim for her compassionate, supportive, and individual approach to teaching.</p> <p>“I love teaching,” she says. “I like to have conversations with my students about what I’m thinking about. It’s not that I’m just imparting knowledge, but I want them to develop a critical way of thinking. I want them to be able to challenge whatever analyses I introduce to them.”</p> <p>Kim also applies this critical-thinking lens to her own scholarship in the history of mathematics. She is particularly interested in studying math this way because the field is often perceived as “all-stable” and contained, when in fact its boundaries have been much more fluid.</p> <p><strong>Mathematics and creativity</strong></p> <p>Kim’s own work re-examines the history of mathematical thought and how it has impacted nonscientific and technical fields in U.S. intellectual life. Her dissertation focuses on the history of mathematics and the ways that mathematicians interacted with artists, humanists, and philosophers throughout the 20th century. She looks at the dialogue and negotiations between different scholars, exploring how they reconfigured the boundaries between academic disciplines.</p> <p>Kim says that this moment in history is particularly interesting because it reframes mathematics as a field that hasn’t operated autonomously, but rather has engaged with humanistic and artistic practices. This creative perspective, she says, suggests an ongoing, historical relationship between mathematics and the arts and humanities that may come as a surprise to those more likely to associate mathematics with technical and military applications, at least in terms of practical uses.</p> <p>“Accepting this clean divide between mathematics and the arts occludes all of these fascinating interactions and conversations between mathematicians and nonmathematicians about what it meant to be modern and creative,” Kim says. One such moment of interaction she explores is between mathematicians and design theorists in the 1930s, who worked together in an attempt to develop and teach a mathematical theory of “aesthetic measure,” a way of ascribing judgments of beauty and taste. &nbsp;</p> <p><strong>Building the foundation</strong></p> <p>With an engineering professor father and a mathematician mother, Kim has long been interested in science and mathematics. However, she says influences from her family, which includes a twin sister who is a classicist and an older sister who studied structural biology, ensured that she would also develop a strong background in the humanities and literature.</p> <p>Kim entered college thinking that she would pursue a technical field, though likely not math itself — she jokes that her math career peaked during her time competing in MATHCOUNTS as a kid. But during her undergraduate years at Brown University, she took a course on the history of science taught by Joan Richards, a professor specializing in the history of mathematics. There, she discovered her interest in studying not just scientific knowledge, but the people who pursue it.</p> <p>After earning a bachelor’s in history at Brown, with a focus in mathematics and science, Kim decided to pursue a doctoral degree. MIT’s HASTS program appealed to her because of its interdisciplinary approach to studying the social and political components of science and technology.</p> <p>“In addition to receiving more formal training in the history of science itself, HASTS trained me in anthropological inquiry, political theory, and all these different kinds of methods that could be brought to bear on the social sciences and humanities more generally,” Kim says.</p> <p>After defending her thesis, Kim will begin a postdoc at Washington University in St. Louis, where she will continue her research and begin converting her dissertation into a book manuscript. She will also be teaching a course she has developed called “Code and Craft,” a course that explores, in a variety of historical contexts, the artful and artisanal components of AI, computing, and otherwise “technical” domains.</p> <p>In her free time, Kim practices taekwondo (she has a first-degree black belt) and enjoys taking long walks through Cambridge, which she says is how she gets some of her best thinking done.</p> PhD student Clare Kim studies the history of mathematics and the ways that mathematicians interacted with artists, humanists, and philosophers throughout the 20th century.Image: Jared CharneyGraduate, postdoctoral, Profile, History, Mathematics, History of science, School of Humanities Arts and Social Sciences, Diversity and inclusion, Technology and society, Race and gender, Program in STS, Students Helping to foster lifelong learning and bonding at MIT A growing number of MIT alumni have taken part in knowledge enhancement programs through MIT Professional Education, as both students and facilitators. Thu, 06 Jun 2019 10:30:01 -0400 MIT Professional Education <p>It’s no secret that MIT’s reputation as a world-class leader in breakthrough education is a major draw for prospective students. Perhaps less well-known is the fact that many graduates return to the MIT community to serve as members of the faculty or staff, or to engage in ongoing learning, to fill in gaps as technology advances and careers grow.</p> <p>In research labs and classrooms across the MIT campus — which is quickly developing into one of the most technologically influential square miles on the planet — dozens of alumni are now leading programs and research aimed at helping to train the next generation of innovators and leaders. A number of alumni are also taking part in knowledge enhancement programs offered through <a href="" target="_blank">MIT Professional Education</a>, as students and facilitators. While each has followed a different path, all share an MIT connection that is second-to-none.&nbsp;</p> <p><strong>The boomerang effect</strong></p> <p><a href="" target="_blank">Gergely "Greg" Sirokman</a>’s first exposure to MIT was in 8th grade, when he attended the Splash program, an annual event where 7th and 8th grade students get to take a variety of STEM-related classes taught by MIT students and community members. Years later, he came back to the Cambridge campus to earn his PhD in inorganic chemistry. Today, Sirokman PhD '07 is a full-time professor at Wentworth Institute of Technology, but his learning experience at MIT continues.</p> <p>“Wentworth offers a very generous education reimbursement package, which means they fund a significant amount of classwork. I decided to take advantage of those benefits and enroll in MIT Professional Education courses,” Sirokman says.</p> <p>Sirokman is among the 84 Institute alumni who have taken advantage of the MIT Professional Education Short Programs over the past five years to actively seek out learning and grow as a member of the MIT community. Since 2007, he has completed a total of seven summer courses, including courses on biofuels, solar energy, and carbon sequestration.<br /> <br /> “These courses allowed me to acquire skills and knowledge I didn’t possess yet as a graduate of MIT, and helped fill holes in my education profile,” Sirokman says. “I immediately turned back around and applied the things I learned to the work I was doing at Wentworth.”<br /> <br /> Today, Sirokman runs a biodiesel lab at Wentworth and is ramping up a project aimed at mitigating the impending energy crisis. The goal is to produce biodiesel fuel from the waste vegetable oil that comes out of the campus cafeteria, and use it to run the fleet of campus vehicles.<br /> <br /> “My mission is to make renewable energy more accessible and train students to have a better understanding and appreciation for renewable energy. Those two things are things I can do better because of the professional education courses I took at MIT,” he says.<br /> <br /> Sirokman shares this piece of advice for the Class of 2019: “The accelerated growth of the technological universe is like a run-away train. Actively seek out learning opportunities to keep up with what is happening in science, technology and engineering. Otherwise, you will get left behind.”</p> <p><strong>Familiar faces carry on MIT’s mission</strong></p> <p>Another reason alumni feel compelled to return to campus is their desire to carry on MIT’s mission to advance knowledge and effect positive change. That was the case for <a href="" target="_blank">Kristala Prather</a> '94, the Arthur D. Little Professor of Chemical Engineering at MIT.</p> <p>“Everyone at MIT is looking to do something special and have an impact by solving some of the world’s biggest challenges,” she says.<br /> <br /> Prather first arrived on campus in 1990, back when there was no internet to share real-time updates on research and network with colleagues. After earning her bachelor of science degree, she went on to earn her PhD at the University of California at Berkley. She subsequently worked at Merck Research Labs for several years, and then decided to return home to her alma mater.<br /> <br /> “I realized what I liked best about my job in industry had to do with mentoring young scientists and training them to be independent researchers,” she says.<br /> <br /> Prather returned as an assistant professor in 2004. Today, her research efforts are centered on the design and assembly of recombinant microorganisms for the production of small molecules, with additional efforts in novel bioprocess design approaches. She also directs an MIT Professional Education course on Fermentation Technology inherited from mentor, Professor Daniel Wang.<br /> &nbsp;<br /> “One of the impacts I found I can make is to provide professionals with more of a foundation to help them understand the theory behind the work they are doing in industry,” Prather says.<br /> <br /> Her advice to the Class of 2019 is to stay connected to MIT: “MIT is such a strong community," she says. "When I first graduated, I didn’t have a sufficient appreciation for just how many opportunities there are to engage with that community – from MIT Professional Education to seminars and symposiums to the Industrial Liason Program. Graduates should think about what brought them to here to begin with, then ask if there’s a way to remain involved, so they can continue to learn and be at the forefront.”</p> <p><strong>Online avenues to lifelong learning</strong></p> <p>Technology has made the world a smaller place and as a result, it is now even easier for alumni to stay connected to campus — even when they live far away. Take Sarah Moran '95 as an example. She graduated from MIT with a BS in mathematics, and now lives in China, where she serves as head of innovation and product at Fidelity Investments.</p> <p>She recently enrolled in MIT Professional Education Digital Plus Programs so that she could learn more about innovation and leadership from seasoned professionals who could help support her transition to a new role at Fidelity.</p> <p>“I had been working in quality assurance for the majority of my career and was looking for a new challenge,” she says. “Engaging in the online learning programs helped open my eyes to other viewpoints and helped position me for long-term success.” Moran says she is not only taking classes for herself, but also to share the experience with colleagues and meet new friends virtually around the world.</p> <p>“We’re proud so many accomplished alums return home to MIT to refuel their knowledge, or to serve as members of faculty in our programs, sharing their research-based knowledge with fellow alums and industry professionals worldwide,” says Bhaskar Pant, executive director at MIT Professional Education.&nbsp;“MIT is after all, a family: an enduring community dedicated to sharing knowledge and giving back for the betterment of humankind.”</p> Left to right: MIT alumni Gergely "Greg" Sirokman PhD '07, Kristala Prather '94, and Sarah Moran '95 Alumni/ae, MIT Professional Education, Mathematics, Chemistry, Chemical engineering, School of Engineering, Classes and programs Mathematical technique quickly tunes next-generation lenses “Metasurfaces” that manipulate light at tiny scales could find uses in cellphone lenses, smart-car sensors, and optical fibers. Tue, 21 May 2019 09:59:59 -0400 Jennifer Chu | MIT News Office <p>Most of us know optical lenses as curved, transparent pieces of plastic or glass, designed to focus light for microscopes, spectacles, cameras, and more. For the most part, a lens’ curved shape has not changed much since it was invented many centuries ago.</p> <p>In the last decade, however, engineers have created flat, ultrathin materials called “metasurfaces” that can perform tricks of light far beyond what traditional curved lenses can do. Engineers etch individual features, hundreds of times smaller than the width of a single human hair, onto these metasurfaces to create patterns that enable the surface as a whole to scatter light very precisely. But the challenge is to know exactly what pattern is needed to produce a desired optical effect.</p> <p>That’s where MIT mathematicians have come up with a solution. In a study published this week in <em>Optics Express</em>, a team reports a new computational technique that quickly determines the ideal makeup and arrangement of millions of individual, microscopic features on a metasurface, to generate a flat lens that manipulates light in a specified way.</p> <p>Previous work attacked the problem by limiting the possible patterns to combinations of predetermined shapes, such as circular holes with different radii, but this approach only explores a tiny fraction of the patterns that can potentially be made.</p> <p>The new technique is the first to efficiently design completely arbitrary patterns for large-scale optical metasurfaces, measuring about 1 square centimeter — a relatively vast area, considering each individual feature is no more than 20 nanometers wide. Steven Johnson, professor of mathematics at MIT, says the computational technique can quickly map out patterns for a range of desired optical effects.</p> <p>“Say you want a lens that works well for several different colors, or you want to take light and instead of focusing it to a spot, make a beam or some sort of hologram or optical trap,” Johnson says. “You can tell us what you want to do, and this technique can come up with the pattern that you should make.”</p> <p>Johnson’s co-authors on the paper are lead author Zin Lin, Raphaël Pestourie, and Victor Liu.</p> <p><strong>Pixel-by-pixel</strong></p> <p>A single metasurface is typically divided into tiny, nanometer-sized pixels. Each pixel can either be etched or left untouched. Those that are etched can be put together to form any number of different patterns.</p> <p>To date, researchers have developed computer programs to search out any possible pixel pattern for small optical devices measuring tens of micrometers across. Such tiny, precise structures can be used to, for instance, trap and direct light in an ultrasmall laser. The programs that determine the exact patterns of these small devices do so by solving Maxwell’s equations — a set of fundamental equations that describe the scattering of light — based on every single pixel in a device, then tuning the pattern, pixel by pixel, until the structure produces the desired optical effect.</p> <p>But Johnson says this pixel-by-pixel simulation task becomes nearly impossible for large-scale surfaces measuring millimeters or centimeters across. A computer would not only have to work with a much larger surface area, with orders of magnitude more pixels, but also would have to run multiple simulations of many possible pixel arrangements to eventually arrive at an optimal pattern.</p> <p>“You have to simulate on a scale big enough to capture the whole structure, but small enough to capture fine details,” Johnson says. “The combination is really a huge computational problem if you attack it directly. If you threw the biggest supercomputer on Earth at it, and you had a lot of time, you could maybe simulate one of these patterns. But it would be a tour de force.”</p> <p><img alt="" src="/sites/" style="width: 500px; height: 500px;" /></p> <p><em><span style="font-size: 10px;">From a randomly patterned metasurface, new technique quickly evolves an ideal pattern to produce a lens with desired optical effects. Credit: Zin Lin</span></em></p> <p><strong>An uphill search</strong></p> <p>Johnson’s team has now come up with a shortcut that efficiently simulates the desired pattern of pixels for large-scale metasurfaces. Instead of having to solve Maxwell’s equations for every single nanometer-sized pixel in a square centimeter of material, the researchers solved these equations for pixel “patches.”</p> <p>The computer simulation they developed starts with a square centimeter of randomly etched, nanometer-sized pixels. They divided the surface into groups of pixels, or patches, and used Maxwell’s equations to predict how each patch scatters light. They then found a way to approximately “stitch” the patch solutions together, to determine how light scatters across the entire, randomly etched surface.</p> <p>From this starting pattern, the researchers then adapted a mathematical technique known as topology optimization, to essentially tweak the pattern of each patch over many iterations, until the final, overall surface, or topology, scatters light in a preferred way.</p> <p>Johnson likens the approach to attempting to find your way up a hill, blindfolded. To produce a desired optical effect, each pixel in a patch should have an optimal etched pattern that should be attained, that could be thought of metaphorically as a peak. Finding this peak, for every pixel in a patch, is considered a topology optimization problem.</p> <p>“For each simulation, we’re finding which way to tweak each pixel,” Johnson says. “You then have a new structure which you can resimulate, and you keep doing this process, each time going uphill until you reach a peak, or optimized pattern.”</p> <p>The team’s technique is able to identify an optimal pattern in just a few hours, compared with traditional pixel-by-pixel approaches which, if applied directly to large metasurfaces, would be virtually intractable.</p> <p>Using their technique, the researchers quickly came up with optical patterns for several “metadevices,” or lenses with varying optical properties, including a solar concentrator that takes incoming light from any direction and focuses it to a single point, and an achromatic lens, which scatters light of different wavelengths, or colors, to the same point, with equal focus.</p> <p>“If you have a lens in a camera, if it’s focused on you, it should be focused for all colors simultaneously,” Johnson says. “The red shouldn’t be in focus but the blue out of focus. So you have to come up with a pattern that scatters all the colors in the same way so they go into the same spot. And our technique is able to come up with a crazy pattern that does that.”</p> <p>Going forward, the researchers are working with engineers, who can fabricate the intricate patterns that their technique maps out, to produce large metasurfaces, potentially for more precise cellphone lenses and other optical applications.</p> <p>“These surfaces could be produced as sensors for cars that drive themselves, or augmented reality, where you need good optics,” Pestourie says. “This technique allows you to tackle much more challenging optical designs.”</p> <p>This research was funded, in part, by the U. S. Army Research Office through the Institute for Soldier Nanotechnologies at MIT .</p> MIT mathematicians have developed a technique that quickly determines the ideal arrangement of millions of individual, microscopic features on a metasurface, to generate a flat lens that manipulates light in a specified way. The team designed a metasurface, at left, etched with millions of features. A zoomed-in image of the lens, right, shows individual features, each etched in a specific way so that, together, they produce a desired optical effect.Credit: Zin LinAlgorithms, Mathematics, Nanoscience and nanotechnology, Photonics, Research, School of Science The kilo is dead. Long live the kilo! An old artifact kept in a vault outside Paris is no longer the standard for the kilogram. Now, nature itself provides the definition. Thu, 16 May 2019 13:02:17 -0400 David L. Chandler | MIT News Office <p>For 130 years, a cylinder made of a platinum-iridium alloy and stored in a suburb of Paris called Saint Cloud has been the official definition of a kilogram, the internationally accepted basic unit of mass. But that will change once and for all on May 20, when for the first time all of the basic units of measurement will be officially defined in terms of &nbsp;atomic properties and fundamental physics constants, rather than specific, human-made objects.</p> <p>The other objects on which physical standards are based, such as the standard meter, were already replaced years ago, but the kilogram — generally known as the kilo for short — turned out to be a harder unit to define in absolute terms. Physicists and engineers have been frustrated, however, by the inevitable imprecision of a unit based on a single physical object.</p> <p>Despite the greatest of precautions, every time the standard kilo was handled — for example, to compare it to another unit that could then be used to calibrate instruments — it would shed some atoms and its mass would be slightly changed. Over its lifetime, that standard kilo is estimated to have lost about 50 micrograms. A better way was needed.</p> <p>Now, instead of a particular lump of metal in a single location, a kilo is to be defined by fixing the numerical value of a fundamental constant of nature known as the Planck constant. This constant relates the energy of a photon to its frequency, and is referred to by the letter h. It is now defined as 6.62607015 times 10<sup>-34</sup> kilograms times square meters per second, thereby defining the kilogram in terms of the second and the meter. Since the second and meter are already defined completely in terms of physical constants, the kilogram is now also defined only in terms of fundamental physical constants.</p> <p>Some may find this new definition complicated and difficult to understand, but Wolfgang Ketterle, a Nobel Prize winner and the John D. MacArthur Professor of Physics at MIT, doesn’t see it that way. “Conceptually, the definition is very simple,” he says.</p> <p>Ketterle notes that the new definition of a kilogram corresponds to the mass of an exact number of particles — a very large number of particles. According to his formulation, it is 1.4755214 times 10<sup>40 </sup>photons (particles of light) of a particular wavelength, which is that of cesium atoms used in atomic clocks.</p> <p>No, that’s not exactly something your butcher can place on a scale to measure out a kilo of ground beef, but it is something that scientists and engineers everywhere in the world — and even aliens on other worlds — could match precisely, without having to carry a scale to Paris to check it.</p> <p>Ketterle sees this important shift in measurement standards as a teachable moment, an opportunity to explain some basic principles to a wide audience. “Ideally, every high school teacher would tell his or her science class about this historic change,” he suggests.</p> <p>To mark the occasion of the official change, which takes place on World Metrology Day, May 20, Ketterle will deliver <a href="">a talk</a> on the new standards at 4 p.m. that day in MIT’s Huntington Hall, room 10-250, where he will explain both the concepts behind the new definition of the kilogram and the techniques for its measurement.</p> <p>Explaining how the other basic units have been defined through basic physical constants is a bit more straightforward than it is for the kilo: The second, for example, is defined as a specific number of vibrations of an atom of cesium. The meter, no longer a metal bar in Saint Cloud, is now defined as the distance that light travels (in a vacuum) in a specific interval of time, namely 1/299,792,458 of a second.</p> <p>Defining the kilo through the mass of photons has to address the fact that photons are constantly whizzing around at the speed of light — after all, they are light — so getting them to sit still on a balance scale is not possible. Instead, they can be trapped between a pair of mirrors, which form an “optical cavity” that keeps them confined. Then, that cavity and its trapped photons can be placed on a balance and measured. The difference between an empty cavity and an identical one full of photons thus provides the mass of the photons themselves. So that’s the concept behind measuring a kilo according to the new definition.&nbsp;</p> <p>However, collecting 10<sup>40 </sup>photons — that’s 1 with 40 zeros after it — is not practical, so instead measurements are made of a much smaller number, and then scaled up step by step. “How do you count to 10<sup>40</sup>?&nbsp; Well, you can’t,” Ketterle says. “However, you can do it by using multiple steps.”</p> <p>He explains this process by analogy to money: “If you win a million dollars, and it is paid in pennies, you don’t want to count pennies. You will first exchange the pennies into dollar bills, and then the dollar bills into 100 dollar bills, and then you count them.”</p> <p>That’s essentially the principle used for measuring mass, he says. “In metrology, something analogous is done by comparing the atomic clock frequency of the cesium atoms to a much higher atomic frequency. Then you use this frequency to measure the mass of the electron or of a single atom, and only then you start counting,” he says.</p> <p>In practice, there are currently two known methods for measuring such masses with great precision. These are known as the Kibble balance and the single-crystal silicon sphere. Both are techniques that laboratories around the world can now use to provide a precise standard for mass and measurements of weights, without ever again having to correlate their measurements with a specific physical object at some central repository.</p> <p>What’s more, now that these units are defined in absolute terms, as soon as better measurement techniques are developed, the accuracy of these measurements will improve accordingly, without the need to revisit the underlying definitions.</p> <p>The new definitions have great power, Ketterle says, because “every new method to count photons or atoms or measure frequencies will further improve the measurements of mass, since mass is no longer connected to an imprecise, man-made artifact.”</p> illustration of a dead kilogramChristine Daniloff, MITPhysics, Mathematics, School of Science, Special events and guest speakers Grad student John Urschel tackles his lifelong balance of math and football in new memoir “Being capable of thinking quantitatively — it’s the single most important thing,” says the former NFL lineman. Wed, 15 May 2019 00:00:00 -0400 Jennifer Chu | MIT News Office <p>It’s been nearly two years since John Urschel retired from the NFL at the age of 26, trading a career as a professional football player at the height of his game for a chance at a PhD in mathematics at MIT. From the looks of it, he couldn’t be happier.</p> <p>The former offensive lineman for the Baltimore Ravens is now a full-time graduate student who spends his days in Building 2, poring over academic papers and puzzling over problems in graph theory, machine learning, and numerical analysis.</p> <p>In his new memoir, “Mind and Matter: A Life in Math and Football,” co-written with his wife, journalist and historian Louisa Thomas, Urschel writes about how he has balanced the messy, physically punishing world of football, with the elegant, cerebral field of mathematics.</p> <p>Urschel presents his life chronologically, through chapters that alternate in focus between math and football, as it often did in real life. For instance, he writes about a moment, following an ecstatic win as part of Penn State’s offensive line, when a coach pulled him aside with a message: With a little more work, he had a shot at the NFL.</p> <p>With that in mind, he writes, “I went home elated. … I left the football building with a new sense of purpose, a mission.” That same night, he opened his laptop and got to work on a paper that he planned to submit with his advisor to a top linear algebra journal. “Suddenly, surprisingly, I had a strange feeling: I felt torn,” he recalls.</p> <p>For those who see Urschel as a walking contradiction, or praise him as an exceptional outlier, he poses, in his book, a challenge:</p> <p>“So often, people want to divide the world into two: matter and energy. Wave and particle. Athlete and mathematician. Why can’t something (or someone) be both?”</p> <p><strong>A refuge in math</strong></p> <p>Before he could speak in full sentences, Urschel’s mother could tell that the toddler had a mind for patterns. To occupy the increasingly active youngster, she gave him workbooks filled with puzzles, which he eagerly devoured at the kitchen table. As he got older, she encouraged him further, and often competitively, with games of reasoning and calculation, such as Monopoly and Battleship. And in the grocery store, she let him keep the change if he could calculate the correct amount before the cashier rang it up.</p> <p>His mother made math a game, and by doing so, lit a lifelong spark. He credits her with recognizing and nurturing his natural interests — something that he hopes to do for his own toddler, Joanna, to whom he dedicates the book.</p> <p>When he was 5 years old, he saw a picture of his father in full pads, as a linebacker for the University of Alberta — his first exposure to the sport of football. From that moment, Urschel wanted to be like his dad, and he wanted to play football.</p> <p>And play he did, though he writes that he wasn’t driven by any innate athletic talent.</p> <p>“The only thing that set me apart from other kids when I played sports was my intensity as a competitor. I couldn’t stand losing — so much so that I would do everything in my power to try to win,” Urschel writes.</p> <p>This fierce drive earned him a full ride to Penn State University, where he forged a lasting connection with the college and its football team. His seemingly disparate talents in math and football started gaining some media attention, as a bright spot for Penn State in an otherwise dark period. (The team was facing national scrutiny as a consequence of the trial of former coach Jerry Sandusky.) But the more news outlets referred to him as a “student-athlete,” the more the moniker grated against him.</p> <p>“[The term ‘student-athlete’] is widely considered a joke of sorts in America,” Urschel says. “But it’s something you can actually do. It takes up a great deal of your time, and it’s not easy. But it is possible to be good at sports while tearing it up in academics.”</p> <p>Urschel proved this in back-to-back years at Penn State, culminating in 2013 with a paper he co-wrote with his advisor, Ludmil Zikatanov, on the spectral bisection of graphs and connectedness, which would later be named the Urschel-Zikatanov theorem. The following year, he was drafted, in the fifth round, by the Baltimore Ravens.</p> <p>He played his entire professional football career as a guard with the Ravens, in 40 games over two years, 13 of which he started. In 2015, in a full-pads practice at training camp with the team, Urschel was knocked flat with a concussion. Just weeks earlier, he had learned that he had been accepted to MIT, where he hoped to pursue a PhD in applied mathematics, during the NFL offseason.</p> <p>In the weeks following the concussion, he writes: “I’d reach for a theorem that I knew I knew, and it wouldn’t be there. I would try to visualize patterns, or to stretch or twist shapes — a skill that had always come particularly easy to me — and I would be unable to see the structures or make things move.”</p> <p>He eventually did regain his facility for math, along with, surprisingly, his need to compete on the field. Despite the possibility of suffering another concussion, he continued to play with the Ravens through 2015. During the off-season, in January 2016, Urschel set foot on the MIT campus to begin work on his PhD.</p> <p><strong>A quantitative mindset</strong></p> <p>“It was like stepping into my personal vision of paradise<em>,”</em> Urschel writes of his first time walking through MIT’s math department in Building 2, noting the chalkboards that lined the hallways, where “casual conversations quickly became discussions of open conjectures<em>.</em>” Urschel was no less impressed by MIT’s football team, whose practices he joined each Monday during that first semester.</p> <p>“These students have so much to do at MIT — it’s a very stressful place,” Urschel says. “And this is Division III football. It’s not high level, and they don’t have packed stands of fans — they’re truly just playing for the love of the game.”</p> <p>He says he was reluctant to return to pro football that summer, and realized throughout that season that he couldn’t wait for Sundays and the prospect of cracking open a math book and tackling problems with collaborators back at MIT and Penn State.</p> <p>An article in the <em>New York Times</em> in July 2017 tipped the scales that had, up until then, kept math and football as equal passions for Urschel. The article outlined a brain study of 111 deceased NFL players, showing 110 of those players had signs of CTE, or chronic traumatic encephalopathy, associated with repeated blows to the head. Urschel writes that the study didn’t change his love for football, but it did make him reevaluate his choices.</p> <p>Two days after reading that article, Urschel announced his retirement from the NFL and packed his bags for a permanent move to MIT.</p> <p>Since then, he has focused his considerable energy on his &nbsp;research, as well as teaching. Last spring, he was a teaching assistant for the first time, in 18.03 (Differential Equations).</p> <p>“I love teaching,” says Urschel, who hopes to be a university math professor and encourages students in class to think creatively, rather than simply memorize the formulas that they’re taught.</p> <p>“I’m fighting against the idea of blindly applying formulas you just learned, and instead teaching students to use their brains,” Urschel says.</p> <p>He’s also making time to visit local high schools to talk math, and STEM education in general.</p> <p>“I’m a visible mathematician,” says Urschel — an understatement to be sure. “I have a responsibility to try to help popularize math, and remove some of its stigma.”</p> <p>His enthusiasm for the subject is highly effective, judging from the overwhelmingly positive reviews from his 18.03 students. Above all, though, he hopes to convey the importance of a “quantitative mindset.”</p> <p>“I don’t care so much if a random person on the street knows the quadratic formula,” Urschel says. “But I do care if they’re able to think through different problems, whether involving loans of two different rates, or how much you need to put in your 401k. Being capable of thinking quantitatively — it’s the single most important thing.”</p> In his new book, John Urschel, former Baltimore Ravens offensive lineman and current PhD candidate in mathematics at MIT, chronicles his life, lived between math and football.Credit: left image courtesy of John Urschel, book image courtesy of Penguin BooksMathematics, School of Science, Sports and fitness, Athletics, STEM education, Books and authors, Graduate, postdoctoral Leaving room for a little improvisation At the piano and in the lab, double major Tony Zhang is driven by curiosity and creativity. Tue, 14 May 2019 14:40:01 -0400 Brittany Flaherty | School of Science <p>Senior Tony Zhang says his curiosity about physics was piqued by an unlikely source: a rubber band.&nbsp;</p> <p>“When I was little, I would stretch rubber bands across cabinet and drawer handles,” says Zhang. “A rubber band produces a different pitch when you pluck it, depending on the material and depending on the tension. So I wondered if I could make an entire scale.” When he succeeded, Zhang says he wanted to know how it worked.&nbsp;</p> <p>Zhang has since pondered the science behind many more observations — and played scales of a more traditional variety. At MIT, he is double-majoring in physics and mathematics with computer science, and minoring in music. Zhang says his double major allowed him to pursue all three of his academic interests, forming what he calls a “math and friends umbrella.”</p> <p>“What draws me to these academic fields is that I tend to be pretty analytical,” he says. “Computers are cool and math is fun, but I really like this particular way of thinking — being able to understand something from first principles.”</p> <p>Trying to understand the science underlying an observation is something Zhang thinks about often in everyday life. Once, while playing a board game with some friends on the 30th or so floor of an apartment building, Zhang says the group noticed that the sun seemed to be setting later than would be expected. Someone suggested it was because they were up so high.&nbsp;</p> <p>“Usually people will think, ‘Maybe that’s it,’ and move on,” says Zhang. “But I do physics, so that is not an acceptable answer.” While everyone else carried on playing the game, Zhang says he worked out that the sunset should be delayed by a few minutes at their current height. “Sometimes problems just stick and then you just have to solve it,” he says. “Or you want to solve it just because you can.”&nbsp;</p> <p>A desire to understand the world around him is what drives Zhang’s studies, as well as his research. Since his junior year, Zhang has worked in the lab of Isaac Chuang focusing on quantum information, as well as atomic, molecular, and optical (AMO) physics. As Zhang explains, while everything is made up of atoms and molecules, AMO physics examines the uniquely atomic and molecular properties that occur at very low temperatures, or when a single atom is trapped in free space, for example. His current research involves trying to implement a simple quantum algorithm in real life through an experiment on a single ion of the element strontium.&nbsp;</p> <p>He also enjoys seeing physics come to life. “Your professors weren't lying when they say atoms behave really weirdly,” Zhang says. “Experimental AMO is an opportunity for you to see all the wacky things they promise you happen in physics theory classes. You can actually see and measure that behavior in real life.”</p> <p><strong>A different note</strong></p> <p>While he came to MIT confident in his academic pursuits, Zhang said he expected to have to give up playing the piano in order to focus on his studies. But Zhang had played since he was 7, and said he started to realize how much he enjoyed it. Impressed by all he learned about MIT’s music program, he auditioned for an Emerson Scholarship. He was selected for the program, which helps fund piano lessons for talented students. He has largely studied with David Deveau, a senior lecturer in music at MIT.&nbsp;</p> <p>“Slowly, instead of phasing it out, piano became an even larger part of my life than it was before coming here,” Zhang says.&nbsp;</p> <p>It’s even become a priority for Zhang to learn about the music departments in the schools he’s applying to for graduate work.</p> <p>“People will ask you whether music informs physics or vice versa. I think the answer is: not really, but I think they're very complementary,” Zhang says. “It’s just very nice to have something completely unrelated to academics to think about and work on.”</p> <p>Beyond the break it affords, Zhang says playing piano was a great way to connect with new people. He says he met one of his closest friends, a violinist, in a piano trio on campus, and that he has found the MIT undergraduate student body to be very musical.&nbsp;</p> <p>During his first year at MIT, Zhang surprised himself by signing up for yet another activity outside of academics. After a friend convinced him to audition, Zhang joined the MIT Asian Dance Team. “I had absolutely zero experience with dance coming into MIT,” he says. “But now I have been dancing my whole time in undergrad — poorly, I will add.”</p> <p>In addition to acting as stress reducers and opportunities to work hard physically, Zhang says these non-academic activities helped him grow as a person. Music, he says, helped him become more observant about how he spends his time and makes decisions about how to maximize his study and practice time. Both music and dance helped him look at himself differently. “I came into MIT not necessarily shy, but also perhaps maybe not fully comfortable with myself,” Zhang says. “I think working on piano very deeply and trying out dance, both have done a lot in helping me feel more confident and comfortable as myself.”&nbsp;</p> <p>Zhang also joined the MIT Association of Taiwanese Students (ATS), and eventually became co-president for his sophomore and junior years. While Zhang isn’t Taiwanese, he said joining ATS was more about building community and spending time with people with similar interests.&nbsp;</p> <p>“There is something so nice about sharing unique parts of your culture with other people who may not have grown up with the same culture, but who also find it interesting,” he says.&nbsp;</p> <p>While each of his pursuits added to his MIT experience, Zhang says he’s the first to admit that it was sometimes more than he could readily manage. “I spent most of my time at MIT doing way too much,” he says. “I was always thinking about commitments.”&nbsp;</p> <p>As a senior, Zhang has a slightly lighter course load and fewer extracurricular activities. He says this reduced plate has allowed him to catch his breath a bit and enjoy his final year at MIT.&nbsp;</p> <p>“College is important to set you up for your future, but it also is an experience to be enjoyed in and of itself,” he says. “It’s amazing to have more free mental time.”&nbsp;</p> <p>Plus, Zhang says, “it's where a lot of unexpected breakthroughs happen.” During rehearsal for a piano trio he was a part of, for example, Zhang remembers a special moment when he let his intuition guide his playing. “I suddenly thought, what if I add pedal, but just like a very small amount of pedal? Maybe it will sound better,” he says. “And it did. If I were just drilling, drilling, drilling sections, I wouldn't have had that realization.”</p> <p>Leaving room for improvisation is just one of many lessons Zhang says he’s learned at MIT. “I decided to come because I thought there would be a lot of people I would click with, and I thought this would be the best place for me to grow,” he says. “All of that has been borne out by the past four years.”</p> <p>After graduation, Zhang plans to attend graduate school to continue studying physics and satisfying his curiosity about the natural world. In physics, he says, there’s still so much to explore.</p> <p>“That’s why science is cool in general: Everything just gains an extra dimension of cool when you know how it works,” Zhang says. “Or when you know that nobody knows how it works."</p> Tony Zhang, a double major in physics and mathematics, says that improvisation is important in science and the arts.Photo: Steph StevensSchool of Science, School of Humanities Arts and Social Sciences, Profile, Students, Undergraduate, Physics, Mathematics, Music, Student life, Music and theater arts Ashwin Sah, Megan Yamoah, and Steven Truong named 2019-20 Goldwater Scholars Three MIT undergraduates honored for their academic achievements. Fri, 10 May 2019 15:30:01 -0400 School of Science <p>Three undergraduate students have been selected for a 2019-20 <a href="">Barry M. Goldwater Scholarship</a>, two in the <a href="">School of Science</a> and one in the <a href="">School of Engineering</a>. In partnership with the U.S. Department of Defense National Defense Education Programs, the Goldwater Foundation gave the award to 496 sophomore and junior students within the United States, chosen from more than 5,000 nominations this year.</p> <p>One of the 62 fellows in mathematics and computer science majors, Ashwin Sah, is not only aiming on continuing his education in mathematics to acquire a PhD but also hopes to teach as a faculty member at a university, researching theoretical mathematics. Now a sophomore in the <a href="">Department of Mathematics</a>, he was previously one of six Putnam Fellows at the Putnam Mathematics Competition and won the gold medal at the International Math Olympiad. Sah produced two papers accepted for publication in research journals, has written several others independently, and solved a 2001 conjecture by Jeff Kahn regarding the maximum number of independent sets in a graph. He is on track to graduate with his bachelor’s degree in three years.</p> <p>Megan Yamoah, a junior in the <a href="">Department of Physics</a>, is among the 360 recipients majoring in natural sciences. In addition to an outstanding academic record, she performed research in two groups and continues in another as a repeat participant in MIT’s Undergraduate Research Opportunities Program. Yamoah has built a control system for a semiconductor, culminating in a patent currently under review. She also helped install dilution refrigerators in a lab on MIT's campus and experiments with them largely on her own, designing and engineering <font size="2"><span style="font-size:10pt;">devices to investigate two-dimensional materials used in novel quantum computing</span></font>. In her future, Yamoah plans to focus on quantum computing. Beyond research, she is a strong student leader in many physics societies and groups on campus.</p> <p>In Course 20 (biological engineering), Steven Truong joins 74 engineers across the country who were granted this year’s Goldwater fellowship. He is a junior in the <a href="">Department of Biological Engineering</a>&nbsp;and is also a double-major in <a href="">Writing</a>. Truong has an&nbsp;outstanding academic record and is also an opinion&nbsp;editor for the <em>MIT Tech</em> newspaper and co-president of the MIT Biological Engineering Undergraduate Board. His&nbsp;research interests lie in studying diabetes, such as developing new ways to deliver insulin to diabetics. He currently works with members of the <a href="">MIT Koch Institute</a> and has also collaborated with the Joslin Diabetes Center, an affiliate of Harvard Medical School, and traveled to Vietnam for a project he co-led.&nbsp;</p> <p>The Barry Goldwater Scholarship and Excellence in Education Program was established by Congress in 1986 to honor Senator Barry Goldwater, who served for 30 years in the U.S. Senate. The program was designed to foster and encourage outstanding students in their pursuit of careers in mathematics, the natural sciences, and engineering, providing recipients with stipends of $7,500 per year to contribute toward their educational expenses.</p> Left to right: Ashwin Sah, Megan Yamoah, and Steven Truong are among just under 500 undergraduate students in the United States to receive 2019 Barry M. Goldwater Scholarships. Photos courtesy of Sah, Yamoah, and TruongSchool of Science, School of Engineering, Mathematics, Physics, Undergraduate Research Opportunities Program (UROP), Biological engineering, Comparative Media Studies/Writing, Koch Institute, School of Humanities Arts and Social Sciences, Awards, honors and fellowships, Undergraduates, Students Gil Strang is still going strong, online and in print After nearly 60 years of teaching at MIT, this math professor surpasses 10 million views on OCW, earns top reviews for his teaching style, and publishes his 12th book. Wed, 08 May 2019 12:30:01 -0400 Sandi Miller | Department of Mathematics <p>MIT’s class 18.06 (<a href="">Linear Algebra</a>) has surpassed 10 million views on <a href="">OpenCourseWare</a> (OCW). That’s the kind of math that makes Professor <a href="">Gilbert Strang</a> one of the most recognized mathematicians in the world.</p> <p>“That was a surprise to me,” says Strang. But not to those at OCW.</p> <p>“He is a favorite; there is no way around it,” says OCW Director Curt Newton. Each month, OCW publishes a list of its most-visited courses, and Newton points out that Strang’s course has always been among the top 10 most-viewed since OCW launched. “He cracked the 10 million number,” he says. “It’s clear that Gil’s teaching has struck just the right chord with learners and educators around the world.”</p> <p>Strang’s 18.06 lectures, posted between 2002-2011, also have more than 3.1 million YouTube views from math students in places like India, China, and Africa, among others. “His lectures are just excellent,” explains math Professor Haynes Miller. To illustrate the video’s massive popularity, Miller recounts a conversation, at the online Electronic Seminar on Mathematics Education, about revising a linear algebra course at the University of Illinois. “In the new version, they do almost no lecturing ... and one reason they feel that they can get away with that is that they can send students to Gil’s lectures on OCW.”</p> <p><strong>A linear path to MIT</strong></p> <p>Strang, the MathWorks Professor of Mathematics, received his BS from MIT in 1955. After earning Rhodes Scholarship to Oxford University and a PhD from the University of California at Los Angeles in 1959, he returned to MIT to teach.</p> <p>Strang began teaching linear algebra in the 1970s, during a time when engineers and scientists wrote large software packages using the finite element method to solve structural problems, computing forces and stresses in solid and fluid mechanics. Strang recalls his “Aha!” moment when he thought about the finite element method of solving partial differential equations using simple trial functions. With scientists generating a huge amount of data, from magnetic resonance scans producing millions of images to microarrays of entire genomes, the goal was to find structure and language to make sense of it all.</p> <p>Once Strang realized that the tools of linear algebra were related to everything from pure math to the internet, he decided to change the way the subject was taught. The 18.06 class soon became popular with science and engineering students, at MIT and around the world. Now in its fifth edition, Strang’s textbook "Introduction to Linear Algebra" has been translated into French, German, Greek, Japanese, and Portuguese. More than 40 years later, about a third of MIT students take this course.</p> <p>“I’m not teaching the math guys who jump over linear algebra,” he says. “18.06 is specifically for engineering and science and economics and management.”</p> <p>Certainly one of the secrets to his OCW success is his teaching style. Strang has a quick smile and an encouraging manner. In his class, he says “please” and “thank you.” To gauge whether students are keeping up, he asks, “Am I OK?” or adds explanations and recaps. He strives for an interactive class by asking questions, and gives intuitions and pictures before presenting a formal proof. And the students seem delighted to see beautiful results emerge from seemingly simple constructions.</p> <p>After a lifetime of teaching at MIT, he is still able to project energy and enthusiasm over his subject. In short, he’s a natural for video.</p> <p>“My original motive for doing this was to encourage other faculty to do it, and maybe show them a new way to teach linear algebra,” he says. His first set of lectures was recorded in 1999 with support from the Lord Foundation of Massachusetts. The videos don’t feature fancy graphics or music, but are an homage to the power of old-school lectures with a chalkboard by a master teacher.</p> <p>The most popular of Strang’s multiple 18.06 OCW versions is the enhanced <a href="">18.06SC “OCW Scholar”</a> version, published in 2011. It adds problem-solving videos by grad students and postdocs patiently explaining a complex subject to a grateful audience, very much in the spirit of Strang’s lectures.</p> <p>“This lecture series is one of the few that I like to watch for fun,” says one commenter. Adds another, “This teacher would be fun to sit down with and have a cup of coffee and conversation.” And a high school teacher says, “He is clear, interesting, and nonthreatening. I watch his linear algebra lessons and wish I could tell him how terrific he is.”</p> <p><strong>A new book</strong></p> <p>OCW <a href="" target="_blank">recently posted</a> 34 videos, along with an introduction, to his relatively new class <a href="">18.065</a> (Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.) To accompany the class, Strang recently released "<a href="">Linear Algebra and Learning from Data</a>," his 12th textbook.</p> <p>Strang is known for his clear yet lively writing, and early reviews confirm that this new book continues his style. Even the book’s cover is evocative. He chose a photo his son Robert took, on Inle Lake in Myanmar, of a man on a boat holding a fishing net encased in a bamboo cage. The man is lifting up what Strang says resembles a neural net.</p> <p>The class was a chance for Strang to expand his linear algebra teachings into the area of deep learning. This class debuted in 2017 when Professor Raj Rao Nadakuditi of the University of Michigan spent his sabbatical teaching 18.065 at MIT. For the class, professor of applied mathematics <a href="">Alan Edelman</a> introduced the powerful language <a href="">Julia</a>, while Strang explained the four fundamental subspaces and the Singular Value Decomposition.</p> <p>“This was linear algebra for signals and data, and it was alive,” says Strang. “More important, this was the student response, too.”</p> <p>Last spring, he started assembling the handouts and online materials into a book. Now in its third year, the class is held in 2-190 and is filled to capacity. In the class and book, Strang starts with linear algebra and moves to optimization by gradient descent, and then to the structure and analysis of deep learning. His goal is to organize central methods and ideas of data science, and to show how the language of linear algebra expresses those ideas.</p> <p>“The new textbook is just the beginning, as the course invites students to ask their own questions and write their own programs. Exams are outlawed. A key point of the course is that it ends with a project from each student — and those projects are wonderful.”</p> <p>His students agree.</p> <p>“Professor Strang structures the class so that ideas seem to flow from the students into proofs,” says senior and math major Jesse Michel. “There’s a nice balance between proofs and examples, so that you know the approaches work in general, while never losing sight of practice. Every class includes a cool math trick or joke that keeps the class laughing. Professor Strang’s energy and emphasis on the exciting points keeps the class on the edge of their seats.”</p> <p><strong>Open means open</strong></p> <p>Haynes Miller says that all MIT faculty are invited to contribute courses to OCW. There are about 2,450 courses on OCW currently, with over 100 having complete video lectures, and more going up as fast as OCW can post them.</p> <p>“OCW began under foundation grants, but is now supported by the provost here at MIT, corporate sponsors, and user donations,” says Miller. “I feel that MIT faculty are extremely lucky to have OpenCourseWare as a publication venue for courseware we design.”</p> Gil Strang teaches 18.06 (Matrix Methods in Data Analysis, Signal Processing, and Machine Learning). OCW will soon post 34 videos to 18.065, and he recently released "Linear Algebra and Learning from Data," his 12th textbook.Photo: Sandi MillerMathematics, School of Science, OpenCourseWare, Linear algebra, Massive open online courses (MOOCs), MITx, EdX, online learning, Classes and programs, Office of Open Learning, Education, teaching and academics, Machine learning Merging cell datasets, panorama style Algorithm stitches multiple datasets into a single “panorama,” which could provide new insights for medical and biological studies. Mon, 06 May 2019 10:59:58 -0400 Rob Matheson | MIT News Office <p>A new algorithm developed by MIT researchers takes cues from panoramic photography to merge massive, diverse cell datasets into a single source that can be used for medical and biological studies.</p> <p>Single-cell datasets profile the gene expressions of human cells —&nbsp;such as a neurons, muscles, and immune cells — to gain insight into human health and treating disease. Datasets are produced by a range of labs and technologies, and contain extremely diverse cell types. Combining these datasets into a single data pool could open up new research possibilities, but that’s difficult to do effectively and efficiently.</p> <p>Traditional methods tend to cluster cells together based on nonbiological patterns — such as by lab or technologies used — or accidentally merge dissimilar cells that appear the same. Methods that correct these mistakes don’t scale well to large datasets, and require all merged datasets share at least one common cell type.</p> <p>In a paper published today in <em>Nature Biotechnology</em>, the MIT researchers describe an algorithm that can efficiently merge more than 20 datasets of vastly differing cell types into a larger “panorama.” The algorithm, called “Scanorama,” automatically finds and stitches together shared cell types between two datasets — like combining overlapping pixels in images to generate a panoramic photo.</p> <p>As long as any other dataset shares one cell type with any one dataset in the final panorama, it can also be merged. But all of the datasets don’t need to have a cell type in common. The algorithm preserves all cell types specific to every dataset.</p> <p>“Traditional methods force cells to align, regardless of what the cell types are. They create a blob with no structure, and you lose all interesting biological differences,” says Brian Hie, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a researcher in the Computation and Biology group. “You can give Scanorama datasets that shouldn’t align together, and the algorithm will separate the datasets according to biological differences.”</p> <p>In their paper, the researchers successfully merged more than 100,000 cells from 26 different datasets containing a wide range of human cells, creating a single, diverse source of data. With traditional methods, that would take roughly a day’s worth of computation, but Scanorama completed the task in about 30 minutes. The researchers say the work represents the highest number of datasets ever merged together.</p> <p>Joining Hie on the paper are: Bonnie Berger, the Simons Professor of Mathematics at MIT, a professor of electrical engineering and computer science, and head of the Computation and Biology group; and Bryan Bryson, an MIT assistant professor of biological engineering.</p> <p><strong>Linking “mutual neighbors”</strong></p> <p>Humans have hundreds of categories and subcategories of cells, and each cell expresses a diverse set of genes. Techniques such as RNA sequencing capture that information in sprawling multidimensional space. Cells are points scattered around the space, and each dimension corresponds to the expression of a different gene.</p> <p>Scanorama runs a modified computer-vision algorithm, called “mutual nearest neighbors matching,” which finds the closest (most similar) points in two computational spaces. Developed at CSAIL, the algorithm was initially used to find pixels with matching features —&nbsp;such as color levels — in dissimilar photos. That could help computers match a patch of pixels representing an object in one image to the same patch of pixels in another image where the object’s position has been drastically altered. It could also be used for stitching vastly different images together in a panorama.</p> <p>The researchers repurposed the algorithm to find cells with overlapping gene expression — instead of overlapping pixel features — and in multiple datasets instead of two. The level of gene expression in a cell determines its function and, in turn, its location in the computational space. If stacked on top of one another, cells with similar gene expression, even if they’re from different datasets, will be roughly in the same locations.</p> <p>For each dataset, Scanorama first links each cell in one dataset to its closest neighbor among all datasets, meaning they’ll most likely share similar locations. But the algorithm only retains links where cells in both datasets are each other’s nearest neighbor — a mutual link. For instance, if Cell A’s nearest neighbor is Cell B, and Cell B’s is Cell A, it’s a keeper. If, however, Cell B’s nearest neighbor is a separate Cell C, then the link between Cell A and B will be discarded.</p> <p>Keeping mutual links increases the likelihood that the cells are, in fact, the same cell types. Breaking the nonmutual links, on the other hand, prevents cell types specific to each dataset from merging with incorrect cell types. Once all mutual links are found, the algorithm stitches all dataset sequences together. In doing so, it combines the same cell types but keeps cell types unique to any datasets separated from the merged cells. “The mutual links form anchors that enable [correct] cell alignment across datasets,” Berger says.</p> <p><strong>Shrinking data, scaling up</strong></p> <p>To ensure Scanorama scales to large datasets, the researchers incorporated two optimization techniques. The first reduces the dataset dimensionality. Each cell in a dataset could potentially have up to 20,000 gene expression measurements and as many dimensions. The researchers leveraged a mathematical technique that summarizes high-dimensional data matrices with a small number of features while retaining vital information. Basically, this led to a 100-fold reduction in the dimensions.</p> <p>They also used a popular hashing technique to find nearest mutual neighbors more quickly. Traditionally, computing on even the reduced samples would take hours. But the hashing technique basically creates buckets of nearest neighbors by their highest probabilities. The algorithm need only search the highest probability buckets to find mutual links, which reduces the search space and makes the process far less computationally intensive. &nbsp;&nbsp;&nbsp;</p> <p>In separate work, the researchers combined Scanorama with another <a href="">technique they developed</a> that generates comprehensive samples — or “sketches” —&nbsp;of massive cell datasets that reduced the time of combining more than 500,000 cells from two hours down to eight minutes. To do so, they generated the “geometric sketches,” ran Scanorama on them, and extrapolated what they learned about merging the geometric sketches to the larger datasets. This technique itself derives from <a href="">compressive genomics</a>, which was developed by Berger’s group.</p> <p>“Even if you need to sketch, integrate, and reapply that information to the full datasets, it was still an order of magnitude faster than combining entire datasets,” Hie says.</p> A new algorithm developed by MIT researchers takes cues from panoramic photography to merge massive, diverse cell datasets into a single source that can be used for medical and biological studies.Image courtesy of the researchers Research, Computer science and technology, Algorithms, Biology, Data, Health sciences and technology, Drug development, Medicine, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Mathematics, Biological engineering, School of Engineering, School of Science New approach could accelerate efforts to catalog vast numbers of cells Data-sampling method makes “sketches” of unwieldy biological datasets while still capturing the full diversity of cell types. Thu, 02 May 2019 00:00:00 -0400 Rob Matheson | MIT News Office <p>Artistic sketches can be used to capture details of a scene in a simpler image. MIT researchers are now bringing that concept to computational biology, with a novel method that extracts comprehensive samples — called “sketches” —&nbsp;of massive cell datasets that are easier to analyze for biological and medical studies.</p> <p>Recent years have seen an explosion in profiling single cells from a diverse range of human tissue and organs —&nbsp;such as a neurons, muscles, and immune cells — to gain insight into human health and treating disease. The largest datasets contain anywhere from around 100,000 to 2 million cells, and growing. The long-term goal of the Human Cell Atlas, for instance, is to profile about 10 billion cells. Each cell itself contains tons of data on RNA expression, which can provide insight about cell behavior and disease progression.</p> <p>With enough computation power, biologists can analyze full datasets, but it takes hours or days. Without those resources, it’s impractical. Sampling methods can be used to extract small subsets of the cells for faster, more efficient analysis, but they don’t scale well to large datasets and often miss less abundant cell types.</p> <p>In a paper being presented next week at the Research in Computational Molecular Biology conference, the MIT researchers describe a method that captures a fully comprehensive “sketch” of an entire dataset that can be shared and merged easily with other datasets. Instead of sampling cells with equal probability, it evenly samples cells from across the diverse cell types present in the dataset.</p> <p>“These are like sketches on paper, where an artist will try to preserve all the important features of a main image,” says Bonnie Berger, the Simons Professor of Mathematics at MIT, a professor of electrical engineering and computer science, and head of the Computation and Biology group.</p> <p>In experiments, the method generated sketches from datasets of millions of cells in a few minutes — as opposed to a few hours —&nbsp;that had far more equal representation of rare cells from across the datasets. The sketches even captured, in one instance, a rare subset of inflammatory macrophages that other methods missed.</p> <p>“Most biologists analyzing single-cell data are just working on their laptops,” says Brian Hie, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a researcher in the Computation and Biology group. “Sketching gives a compact summary of a very large dataset that tries to preserve as much biological information as possible … so people don’t need to use so much computational power.”</p> <p>Joining Hie and Berger on the paper are: CSAIL PhD student Hyunghoon Cho; Benjamin DeMeo, a graduate student at MIT and Harvard Medical School; and Bryan Bryson, an MIT assistant professor of biological engineering.</p> <p><strong>Plaid coverings</strong></p> <p>Humans have hundreds of categories and subcategories of cells, and each cell expresses a diverse set of genes. Techniques such as RNA sequencing capture all cell information in massive tables, where each row represents a cell and each column represents some measurement of gene expression. Cells are points scattered around a sprawling multidimensional space where each dimension corresponds to the expression of a different gene.</p> <p>As it happens, cell types with similar gene diversity — both common and rare — form similar-sized clusters that take up roughly the same space. But the density of cells within those clusters varies greatly: 1,000 cells may reside in a common cluster, while the equally diverse rare cluster will contain 10 cells. That’s a problem for traditional sampling methods that extract a target-size sample of single cells.</p> <p>“If you take a 10-percent sample, and there are 10 cells in a rare cluster and 1,000 cells in a common cluster, you’re more likely to grab tons of common cells, but miss all rare cells,” Hie says. “But rare cells can lead to important biological discoveries.”</p> <p>The researchers modified a class of algorithm that lays shapes over datasets. Their algorithm covers the entire computational space with what they call a “plaid covering,” which is like a grid of equal-sized squares but in many dimensions. It only lays these multidimensional squares where there’s at least one cell, and skips over any empty regions. In the end, the grid’s empty columns will be much wider or skinnier than occupied columns — hence the “plaid” description. That technique saves tons of computation to help the covering scale to massive datasets.</p> <p><strong>Capturing rare cells</strong></p> <p>Occupied squares may contain only one cell or 1,000 cells, but they will all have the exact same sampling weight. The algorithm then finds a target sample — of, say, 20,000 cells —&nbsp;by selecting a set number of cells from each occupied square uniformly, at random. The resulting sketch contains a far more equal distribution of cell types — for example, 10 common cells from a cluster of 100 and eight rare cells from a cluster of 10.</p> <p>“We take advantage of these cell types occupying similar volumes of space,” Hie says. “Because we sample according to volume, instead of density, we get a more even coverage of the biological space … and we’re naturally preserving the rare cell types.”</p> <p>They applied their sketching method to a dataset of around 250,000 umbilical cord cells that contained two subsets of a rare macrophages — inflammatory and anti-inflammatory. All other traditional sampling methods clustered both subsets together, while the sketching method separated them. Additional in-depth studies of these macrophage subpopulations could help reveal insight into inflammation and how to modulate inflammatory processes in response to disease, the researchers say.</p> <p>“That’s a benefit in working at the interface of fields,” Berger says. “We’re trained as mathematicians, but we understand what biological data science problems are, so we can bring the best technologies to their analysis.”</p> <p>“Geometric sketching is a promising tool for many practical applications of single-cell technology,” says Teresa Przytycka, a senior investigator in the Algorithmic Methods in Computational and Systems Biology group at the National Institutes of Health. “Thanks to the [single-cell RNA sequencing] technologies, many new cell types have been already discovered in several human tissues. Analyzes of even larger single-cell data have the potential to reveal additional rare cell subpopulations. … [S]ingle-cell data tends to be quite complex and its analysis often calls for applying advanced and computationally demanding algorithms. Geometric sketching will allow efficient application of such algorithms by reducing the number of data points while preserving the most relevant information.”</p> MIT researchers have developed a method for analyzing massive sets of data on single cells, that captures comprehensive samples — called “sketches” — while retaining retain cell diversity. Research, Computer science and technology, Algorithms, Biology, Data, Health sciences and technology, Drug development, Medicine, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Mathematics, Biological engineering, School of Engineering, School of Science Peter Shor wins 2018 Micius Quantum Prize Shor awarded the $150,000 prize, named after a fifth-century B.C. Chinese scientist, for his groundbreaking theoretical work in the field of quantum computation. Fri, 26 Apr 2019 13:10:00 -0400 Sandi Miller | Department of Mathematics <p><a href="">Peter Shor</a>, the Morss Professor of Applied Mathematics at MIT, has received the<strong>&nbsp;</strong>2018 <a href="">Micius Quantum Prize</a>, which is awarded within the field of quantum computation.</p> <p>Shor was nominated for his groundbreaking theoretical work on the factoring algorithm and quantum error correction. Shor, who received his PhD in applied mathematics from MIT in 1985&nbsp;under the direction of <a href="">Tom Leighton</a>, is known for his work on&nbsp;quantum computation. <a href="" title="Shor's algorithm">Shor's algorithm</a> is a groundbreaking integer-factoring algorithm that he&nbsp;developed in the mid-1990s, which proves a quantum computer can calculate the prime factors of a large number exponentially faster than a classical computer.</p> <p>“Peter Shor's quantum algorithms, starting from his factoring algorithm — known as Shor's algorithm — has revolutionized the field of quantum&nbsp;computing,” says <a href="">Michel Goemans</a>, department head and professor of mathematics. “One could even say that the field would never have taken off&nbsp;without his deep and significant contributions to it.”</p> <p>The algorithm is designed to use a quantum computer to quickly break through the <a href="">RSA (Rivest-Shamir-Adelman) encryption algorithm</a>, which is based on the difficulty of prime factorization, a major concern for the security of classical computing systems. Shor also introduced quantum error-correcting codes and fault-tolerant quantum computation to protect quantum states against decoherence and noise.</p> <p>He will receive 1 million Chinese yuan (about $150,000) as part of his award, which he expects to put toward his continued research into quantum cryptography and quantum information theory. One idea:&nbsp;“I’m thinking about how quantum information relates to black holes,” he says.</p> <p>More importantly, says&nbsp;Shor, the Micius Quantum Prize “will draw a lot of attention to the field.”</p> <p>“It’s an exciting time,” he says. “The U.S. government and the Chinese government are putting a lot of money into quantum computing. Experimentalists are starting to build quantum computers that are reaching the point where they can’t be simulated by digital computers. People are building very small prototypes, as experiments to see how big quantum computers will behave.”</p> <p>Shor has received many other awards for his quantum computing research, including the <a href="">Dirac Medal</a> of the International Centre for Theoretical Physics, the <a href="">IEEE Eric E. Sumner Award</a>, for Outstanding Contributions to Communications Technology, and the <a href="">Nevanlinna Prize</a>. He also is affiliated with the Computer Science and Artificial Intelligence Laboratory (<a href="" title="CSAIL">CSAIL</a>) and the <a href="">Center for Theoretical Physics</a>.</p> <p>The Micius Quantum Prize recognizes significant science advances ranging from early conceptual contributions to recent experimental breakthroughs in the field of quantum communications, quantum simulation, quantum computation, and quantum metrology. Funded by private entrepreneurs, the Micius Quantum Foundation was named after the fifth-century B.C. Chinese scientist — who is also known as Mozi —&nbsp;who used a pinhole to discover that light travels in straight lines, and who wrote an earlier version of what later became Newton’s first law of motion.</p> <p>The newly announced 2018 and 2019 laureates represent the inaugural winners of the Micius Quantum Prize. Other 2018 Micius laureates are Juan Ignacio Cirac, David Deutsch, and Peter Zoller, for their theoretical work on quantum algorithms and physical architectures of quantum computers and simulators; and Rainer Blatt and David Wineland, for experiments that demonstrated fundamental elements of quantum computing with trapped ions.</p> <p>The 2019 Micius Quantum Prizes were announced&nbsp;within the field of quantum communication: Charles Bennett, Gilles Brassard, Artur Ekert, and Stephen Wiesner, for their inventions of quantum cryptography, and Jian-Wei Pan and Anton Zeilinger for experiments that enabled practically secure and large-scale quantum communications.</p> <p>The award ceremony for 2018 and 2019 prizes will be held on Sept.&nbsp;20, during the International Conference on Emerging Quantum Technologies in Hefei, China.</p> Peter Shor has bee honored with the 2018 Micius Quantum Prize. He is known for Shor's algorithm, a groundbreaking integer-factoring algorithm relating to quantum computing.Photo: Rosalee ZammutoSchool of Science, Mathematics, Quantum computing, Computer science and Artificial Intelligence Lab (CSAIL), Awards, honors and fellowships, Faculty, Center for Theoretical Physics Three from MIT awarded 2019 Guggenheim Fellowships Professors David Jerison, Hong Liu, and Seth Mnookin are among 168 recognized. Mon, 22 Apr 2019 17:10:01 -0400 Laura Carter | School of Science <p>Three MIT faculty members are among 168 people out of 3,000 applicants granted a fellowship by the <a href="">John Simon Guggenheim Memorial Foundation</a>. The foundation's announcement notes that the awardees were chosen based on their prior accomplishments and strong future potential. The MIT recipients are David Jerison and Hong Liu in the School of Science, and Seth Mnookin in the School of Humanities, Arts, and Social Sciences.</p> <p>“It’s exceptionally satisfying to name 168 new Guggenheim Fellows,” says Edward Hirsch, president of the Guggenheim Foundation. “These artists and writers, scholars and scientists, represent the best of the best.” This year’s recipients join more than 18,000 extraordinary individuals who previously received this honor.</p> <p><a href="">David Jerison</a>, a professor in the <a href="">Department of Mathematics</a>, has received <a href="">a Guggenheim fellowship</a> to study interfaces that divide regions in optimal ways; these can be applied to situations where minimized energy or cost is important. Previously, he was one of 10 principal investigators awarded a 2018 Simons Foundation Collaboration Grant. He is also a recipient of a Sloan Foundation Research Fellowship, the Bergman Prize, and a National Science Foundation Presidential Young Investigator Award. He is also currently a fellow of the American Academy of Arts and Sciences and the American Mathematical Society, and vice president of the American Mathematical Society. A dedicated teacher, Jerison became an MIT MacVicar Faculty Fellow in 2004 and has designed many popular courses for MIT’s Open Courseware, <em>MITx</em> and edX.</p> <p><a href="">Hong Liu</a> will be applying <a href="">his fellowship</a> to his interdisciplinary research on black holes, turbulence, and quantum many-body systems. A professor in the <a href="">Department of Physics</a>, Liu has helped found interconnections between gravitational, nuclear and condensed matter physics, one of the first to use string theory to study quark-gluon plasma and identify similarities between black holes and superconductors. Prior to this fellowship, he was elected an Alfred Sloan Fellow, an Outstanding Junior Investigator by the Department of Energy, and a Simons Fellow.</p> <p><a href="">Seth Mnookin</a> is the director of the <a href="">Graduate Program in Science Writing</a> and a professor in the <a href="">Comparative Media Studies/Writing</a> program. He has authored three books to date: his first was recognized as Best Book of the Year by <em>The Washington Post,</em> his second reached <em>The New York Times</em>' bestseller list, and the most recent won the “Science in Society Award” from the National Association of Science Writers. <a href="">The Guggenheim Fellowship</a> is the most recent award for Mnookin, whose other accolades include the American Medical Writers Association prize for best story of 2014 and his election to the board of the National Association of Science Writers.</p> Guggenheim Fellowship recipients (left to right) David Jerison, Hong Liu, and Seth MnookinPhotos: David Jerison, Hong Liu, and Seth MnookinSchool of Science, School of Humanities Arts and Social Sciences, Mathematics, Physics, Science writing, Comparative Media Studies/Writing, Awards, honors and fellowships, Faculty Q&amp;A: Professor Gigliola Staffilani on women in mathematics More than a decade after creating the Celebration of Women in Mathematics at MIT, Staffilani talks about the present and future of women in the field. Wed, 03 Apr 2019 14:10:00 -0400 Laura Carter | School of Science <p><em>In 2008, Gigliola Staffilani, the Abby Rockefeller Mauzé Professor in the Department of Mathematics, and former MIT assistant professor Katrin Wehrheim spearheaded the Celebration of Women in Mathematics at MIT,&nbsp;a two-day event focused on MIT’s role as a leading educator of women mathematicians. Shortly after, Staffilani and Wehrheim founded MIT’s Women in Mathematics, a group of graduate students, postdocs, and faculty dedicated to the professional and personal success of women mathematicians. They also initiated the Weeks Lectures, aptly named after Dorothy Weeks, the first woman to receive a doctorate from the Department of Mathematics in 1930. </em></p> <p><em>Prior to joining MIT in 2002, Staffilani taught on the faculties of Stanford University and Brown University after receiving her PhD from the University of Chicago in 1995. Specializing in dispersive nonlinear partial differential equations, her work in theoretical mathematics investigates tools to analyze complex equations coming from physics for which explicit solutions are not available. Staffilani is an elected member of the American Academy of Arts and Sciences, a fellow of the American Mathematical Society and a Guggenheim fellow.&nbsp;More than 10 years after the 2008 celebration, Staffilani answers&nbsp;questions about the future of women in her field.&nbsp;</em></p> <p><strong>Q: </strong>What is the status of women in mathematics today?</p> <p><strong>A:</strong> This is a very difficult question to answer because it is really multi-dimensional. From a personal perspective, I can say that there is definitely a larger presence of women in math departments than when I first started at MIT, in particular the number of tenured professors. When I was a graduate student, Stanford, the University of Chicago, and Harvard University had none,&nbsp;Princeton had a couple, and MIT hired the first one shortly after in applied math. The situation now is much, much better. Even Harvard, the last to capitulate,&nbsp;has one female senior professor. And at MIT, we now have four tenured women [Bonnie Berger-Leighton, Ju-Lee Kim, Nike Sun, and Staffilani].</p> <p>I believe that the presence of women faculty is fundamental to train, attract, and retain more women in the field —&nbsp;it is a chain reaction.&nbsp;But an academic career is just one path. Many fantastic women who are successful undergraduates here at MIT and potentially could become top-caliber professional researchers in mathematics may decide to take very well-paying jobs in industry or exciting startups, and this is great, too.</p> <p><strong>Q:</strong> How have things changed (or not) in terms of gender parity in mathematics, in research, academic, or industry positions?</p> <p><strong>A: </strong>My sense is that, in terms of stipends and grants, women are doing as well as men in academia, at least at MIT. My sense is also that women in academia tend to negotiate less or look less often for outside offers, so it could be that later in their careers, they may start making less compared to male colleagues who are more actively looking to be retained. I do not know what happens in industry, but in many other aspects, women are doing worse —&nbsp;they are not proportionally represented as speakers at conferences&nbsp;and yet&nbsp;they are asked to serve on all sorts of committees and panels because&nbsp;institutions often ask for the presence of at least one woman. And they are disproportionately asked to write letters of recommendation because often institutions ask to have at least a couple of women writers.&nbsp;</p> <p><strong>Q: </strong>What does the future of women in math look like?</p> <p><strong>A: </strong>I am optimistic. I see more women in departments with strong research records who will be able to train other talented women and serve as positive role models. But often, I read articles and blog posts from women who, rightly-so, express discontent with all sorts of implicit biases, sexual harassment, discrimination, and so on. For young women out there, it must be very discouraging. I think it is up to us senior women to convince them that, in spite of all of the potential negatives, being a professional woman mathematician is really fun and rewarding.</p> <p><strong>Q:</strong> Which female mathematician, living or dead, would you most want to have a conversation with and why?</p> <p><strong>A: </strong>I wish I had talked more with Catherine Morawetz when she was alive. In fact, I had received a card from her few months before she passed, and I was planning to go to New York City to see her; but unfortunately, I didn’t make it in time. She was an amazing mathematician; she did most of her research as a research affiliate at NYU while her husband had a professorship. I would have liked to know how she felt about not being recognized for her incredible research until much, much later, when she was finally given a professorship. I would have liked to know if the stress and commitments, which usually take focus and time away from research, may in fact, have helped her research progress. And I would have liked to know how she managed the demands that come from children — if, like for me, their presence actually helped in smoothing out the usual obsession that comes from a brain fully engaged with a hard-to-crack math problem. Most of all, I would have liked to know what she loved about being a mathematician.&nbsp;</p> <p>[Morawetz SM '46 was a Canadian mathematician who received her master’s degree at MIT, working at the Institute later as a research associate after receiving her doctorate from New York University. She returned to the NYU’s Courant Institute of Mathematical Sciences, where she served as its director. She is acclaimed for her work on partial differential equations regarding fluid dynamics. In 1981, she was the first woman to deliver the American Mathematical Society’s Gibbs Lecture and in 1998, she became the first female mathematician to receive the National Medal of Science.]</p> Department of Mathematics Professor Gigliola Staffilani is a pioneering role model, mentor, and advocate for women in mathematics.Photo: Laura CarterSchool of Science, Mathematics, Women in STEM, Faculty, Women, Diversity and inclusion School of Science announces 2019 Infinite Mile Awards Ten staff members in the School of Science are recognized for going above and beyond their job descriptions to support a better Institute. Tue, 02 Apr 2019 10:12:01 -0400 School of Science <p>The MIT <a href="">School of Science</a> has announced the winners of the 2019 Infinite Mile Award, which is presented annually to staff members within the school who demonstrate exemplary dedication to making MIT a better place.</p> <p>Nominated by their colleagues, these winners are notable for their unrelenting and extraordinary hard work in their positions, which can include mentoring fellow community members, innovating new solutions to problems big and small, building their communities, or going far above and beyond their job descriptions to support the goals of their home departments, labs, and research centers.</p> <p>The 2019 Infinite Mile Award winners are:</p> <p>Christine Brooks, an administrative assistant in the <a href="">Department of Chemistry</a>, nominated by Mircea Dincă and several members of the Dincă, Schrock, and Cummins groups;</p> <p>Annie Cardinaux, a research specialist in the <a href="">Department of Brain and Cognitive Sciences</a>, nominated by Pawan Sinha;</p> <p>Kimberli DeMayo, a human resources consultant in the <a href="">Department of Mathematics</a>, nominated by Nan Lin, Dennis Porche, and Paul Seidel, with support from several other faculty members;</p> <p>Arek Hamalian, a technical associate at the <a href="">Picower Institute for Learning and Memory</a>, nominated by Susumu Tonegawa;</p> <p>Jonathan Harmon, an administrative assistant in the <a href="">Department of Mathematics</a>, nominated by Pavel Etingof and Kimberli DeMayo, with support from several other faculty members;</p> <p>Tanya Khovanova, a lecturer in the <a href="">Department of Mathematics</a>, nominated by Pavel Etingof, David Jerison, and Slava Gerovitch;</p> <p>Kelley Mahoney, an SRS financial staff member in the <a href="">Kavli Institute for Astrophysics and Space Research</a>, nominated by Sarah Brady, Michael McDonald, Anna Frebel, Jacqueline Hewitt, Jack Defandorf, and Stacey Sullaway;</p> <p>Walter Massefski, the director of instrumentation facility in the <a href="">Department of Chemistry</a>, nominated by Timothy Jamison and Richard Wilk;</p> <p>Raleigh McElvery, a communications coordinator in the <a href="">Department of Biology</a>, nominated by Vivian Siegel with support from Amy Keating, Julia Keller, and Erika Reinfeld; and</p> <p>Kate White, an administrative officer in the <a href="">Department of Brain and Cognitive Sciences</a>, nominated by Jim DiCarlo, Michale Fee, Sara Cody-Larnard, Rachel Donahue, Federico Chiavazza, Matthew Regan, Gayle Lutchen, and William Lawson.</p> <p>The recipients will receive a monetary award in addition to being honored at a celebratory reception, along with their peers, family and friends, and the recipients of the 2019 Infinite Kilometer Award this month.</p> School of Science, Chemistry, Brain and cognitive sciences, Mathematics, Picower Institute for Learning and Memory, Kavli Institute, Biology, Staff, Awards, honors and fellowships, Community