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      MIT community in 2022: A year in review

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      In 2022, MIT returned to a bit of normalcy after the challenge of Covid-19 began to subside. The Institute prepared to bid farewell to its president and later announced his successor; announced five flagship projects in a new competition aimed at tackling climate’s greatest challenges; made new commitments toward ensuring support for diverse voices; and celebrated the reopening of a reimagined MIT Museum — as well as a Hollywood blockbuster featuring scenes from campus. Here are some of the top stories in the MIT community this year.

      Presidential transition

      In February, MIT President L. Rafael Reif announced that he planned to step down at the end of 2022. In more than 10 years as president, Reif guided MIT through a period of dynamic growth, greatly enhancing its global stature and magnetism. At the conclusion of his term at the end of this month, Reif will take a sabbatical, then return to the faculty of the Department of Electrical Engineering and Computer Science. In September, Reif expressed his gratitude to the MIT community at an Institute-wide dance celebration, and he was honored with a special MIT Dome lighting earlier this month.

      After an extensive presidential search, Sally Kornbluth, a cell biologist and the current provost of Duke University, was announced in October as MIT’s 18th president. Following an introduction to MIT that included a press conference, welcoming event, and community celebration, Kornbluth will assume the MIT presidency on Jan. 1, 2023.

      In other administrative transitions: Cynthia Barnhart was appointed provost after Martin Schmidt stepped down to become president of Rensselaer Polytechnic Institute; Sanjay Sarma stepped down as vice president for open learning after nine years in the role; professors Brent Ryan and Anne White were named associate provosts, while White was also named associate vice president for research administration; and Agustín Rayo was named dean of the School of Humanities, Arts, and Social Sciences.

      Climate Grand Challenges

      MIT announced five flagship projects in its first-ever Climate Grand Challenges competition. These multiyear projects focus on unraveling some of the toughest unsolved climate problems and bringing high-impact, science-based solutions to the world on an accelerated basis. Representing the most promising concepts to emerge from the two-year competition that yielded 27 finalist projects, the five flagship projects will receive additional funding and resources from MIT and others to develop their ideas and swiftly transform them into practical solutions at scale.

      CHIPS and Science Act

      President Reif and Vice President for Research Maria Zuber were among several MIT representatives to witness President Biden’s signing of the $52 billion “CHIPS and Science” bill into law in August. Reif helped shape aspects of the bill and was a vocal advocate for it among university and government officials, while Zuber served on two government science advisory boards during the bill’s gestation and consideration. Earlier in the year, MIT.nano hosted U.S. Secretary of Commerce Gina Raimondo, while MIT researchers released a key report on U.S. microelectronics research and manufacturing.

      MIT Morningside Academy for Design

      Supported by a $100 million founding gift, the MIT Morningside Academy for Design launched as a major interdisciplinary center that aims to build on the Institute’s leadership in design-focused education. Housed in the School of Architecture and Planning, the academy provides a hub that will encourage design work at MIT to grow and cross disciplines among engineering, science, management, computing, architecture, urban planning, and the arts.

      Reports of the Institute

      A number of key Institute reports and announcements were released in 2022. They include: an announcement of the future of gift acceptance for MIT: an announcement of priority MIT investments; a new MIT Values Statement; a renewed commitment to Indigenous scholarship and community; the Strategic Action Plan for Belonging, Achievement, and Composition; a report on MIT’s engagement with China; a report of the Working Group on Reimagining Public Safety at MIT; a report of the Indigenous Working Group; and a report of the Ad Hoc Committee on Arts, Culture, and DEI.

      Nobel Prizes

      MIT affiliates were well-represented among new and recent Nobel laureates who took part in the first in-person Nobel Prize ceremony since the start of the Covid-19 pandemic. MIT-affiliated winners for 2022 included Ben Bernanke PhD ’79, K. Barry Sharpless, and Carolyn Bertozzi. Winners in attendance from 2020 and 2021 included Professor Joshua Angrist, David Julius ’77, and Andrea Ghez ’87.

      New MIT Museum

      A reimagined MIT Museum opened this fall in a new 56,000-square-foot space in the heart of Cambridge’s Kendall Square. The museum invites visitors to explore the Institute’s innovations in science, technology, engineering, arts, and math — and to take part in that work with hands-on learning labs and maker spaces, interactive exhibits, and venues to discuss the impact of science and technology on society.

      “Wakanda Forever”

      In November, the Institute Office of Communications and the Division of Student Life hosted a special screening of Marvel Studios’ “Black Panther: Wakanda Forever.” The MIT campus had been used as a filming location in summer 2021, as one of the film’s characters, Riri Williams (also known as Ironheart), is portrayed as a student at the Institute.

      In-person Commencement returns

      After two years of online celebrations due to Covid-19, MIT Commencement returned to Killian Court at the end of May. World Trade Organization Director-General Ngozi Okonjo-Iweala MCP ’78, PhD ’81 delivered the Commencement address, while poet Kealoha Wong ’99 spoke at a special ceremony for the classes of 2020 and 2021.

      Students win distinguished fellowships

      As in previous years, MIT students continued to shine. This year, exceptional undergraduates were awarded Fulbright, Marshall, Mitchell, Rhodes, and Schwarzman scholarships.

      Remembering those we’ve lost

      Among MIT community members who died this year were Robert Balluffi, Louis Braida, Ashton Carter, Tom Eagar, Dick Eckaus, Octavian-Eugen Ganea, Peter Griffith, Patrick Hale, Frank Sidney Jones, Nonabah Lane, Leo Marx, Bruce Montgomery, Joel Moses, Brian Sousa Jr., Mohamed Magdi Taha, John Tirman, Richard Wurtman, and Markus Zahn.

      In case you missed it:

      Additional top community stories of 2022 included MIT students dominating the 82nd Putnam Mathematical Competition, an update on MIT’s reinstating the SAT/ACT requirement for admissions, a new mathematics program for Ukrainian students and refugees, a roundup of new books from MIT authors, the renaming of the MIT.nano building, an announcement of winners of this year’s MIT $100K Entrepreneurship Competition, the new MIT Wright Brothers Wind Tunnel, and MIT students winning the 45th International Collegiate Programming Contest for the first time in 44 years. More

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      MIT Policy Hackathon produces new solutions for technology policy challenges

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      Almost three years ago, the Covid-19 pandemic changed the world. Many are still looking to uncover a “new normal.”

      “Instead of going back to normal, [there’s a new generation that] wants to build back something different, something better,” says Jorge Sandoval, a second-year graduate student in MIT’s Technology and Policy Program (TPP) at the Institute for Data, Systems and Society (IDSS). “How do we communicate this mindset to others, that the world cannot be the same as before?”

      This was the inspiration behind “A New (Re)generation,” this year’s theme for the IDSS-student-run MIT Policy Hackathon, which Sandoval helped to organize as the event chair. The Policy Hackathon is a weekend-long, interdisciplinary competition that brings together participants from around the globe to explore potential solutions to some of society’s greatest challenges. 

      Unlike other competitions of its kind, Sandoval says MIT’s event emphasizes a humanistic approach. “The idea of our hackathon is to promote applications of technology that are humanistic or human-centered,” he says. “We take the opportunity to examine aspects of technology in the spaces where they tend to interact with society and people, an opportunity most technical competitions don’t offer because their primary focus is on the technology.”

      The competition started with 50 teams spread across four challenge categories. This year’s categories included Internet and Cybersecurity, Environmental Justice, Logistics, and Housing and City Planning. While some people come into the challenge with friends, Sandoval said most teams form organically during an online networking meeting hosted by MIT.

      “We encourage people to pair up with others outside of their country and to form teams of different diverse backgrounds and ages,” Sandoval says. “We try to give people who are often not invited to the decision-making table the opportunity to be a policymaker, bringing in those with backgrounds in not only law, policy, or politics, but also medicine, and people who have careers in engineering or experience working in nonprofits.”

      Once an in-person event, the Policy Hackathon has gone through its own regeneration process these past three years, according to Sandoval. After going entirely online during the pandemic’s height, last year they successfully hosted the first hybrid version of the event, which served as their model again this year.

      “The hybrid version of the event gives us the opportunity to allow people to connect in a way that is lost if it is only online, while also keeping the wide range of accessibility, allowing people to join from anywhere in the world, regardless of nationality or income, to provide their input,” Sandoval says.

      For Swetha Tadisina, an undergraduate computer science major at Lafayette College and participant in the internet and cybersecurity category, the hackathon was a unique opportunity to meet and work with people much more advanced in their careers. “I was surprised how such a diverse team that had never met before was able to work so efficiently and creatively,” Tadisina says.

      Erika Spangler, a public high school teacher from Massachusetts and member of the environmental justice category’s winning team, says that while each member of “Team Slime Mold” came to the table with a different set of skills, they managed to be in sync from the start — even working across the nine-and-a-half-hour time difference the four-person team faced when working with policy advocate Shruti Nandy from Calcutta, India.

      “We divided the project into data, policy, and research and trusted each other’s expertise,” Spangler says, “Despite having separate areas of focus, we made sure to have regular check-ins to problem-solve and cross-pollinate ideas.”

      During the 48-hour period, her team proposed the creation of an algorithm to identify high-quality brownfields that could be cleaned up and used as sites for building renewable energy. Their corresponding policy sought to mandate additional requirements for renewable energy businesses seeking tax credits from the Inflation Reduction Act.

      “Their policy memo had the most in-depth technical assessment, including deep dives in a few key cities to show the impact of their proposed approach for site selection at a very granular level,” says Amanda Levin, director of policy analysis for the Natural Resources Defense Council (NRDC). Levin acted as both a judge and challenge provider for the environmental justice category.

      “They also presented their policy recommendations in the memo in a well-thought-out way, clearly noting the relevant actor,” she adds. This clarity around what can be done, and who would be responsible for those actions, is highly valuable for those in policy.”

      Levin says the NRDC, one of the largest environmental nonprofits in the United States, provided five “challenge questions,” making it clear that teams did not need to address all of them. She notes that this gave teams significant leeway, bringing a wide variety of recommendations to the table. 

      “As a challenge partner, the work put together by all the teams is already being used to help inform discussions about the implementation of the Inflation Reduction Act,” Levin says. “Being able to tap into the collective intelligence of the hackathon helped uncover new perspectives and policy solutions that can help make an impact in addressing the important policy challenges we face today.”

      While having partners with experience in data science and policy definitely helped, fellow Team Slime Mold member Sara Sheffels, a PhD candidate in MIT’s biomaterials program, says she was surprised how much her experiences outside of science and policy were relevant to the challenge: “My experience organizing MIT’s Graduate Student Union shaped my ideas about more meaningful community involvement in renewables projects on brownfields. It is not meaningful to merely educate people about the importance of renewables or ask them to sign off on a pre-planned project without addressing their other needs.”

      “I wanted to test my limits, gain exposure, and expand my world,” Tadisina adds. “The exposure, friendships, and experiences you gain in such a short period of time are incredible.”

      For Willy R. Vasquez, an electrical and computer engineering PhD student at the University of Texas, the hackathon is not to be missed. “If you’re interested in the intersection of tech, society, and policy, then this is a must-do experience.” More

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      Coordinating climate and air-quality policies to improve public health

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      As America’s largest investment to fight climate change, the Inflation Reduction Act positions the country to reduce its greenhouse gas emissions by an estimated 40 percent below 2005 levels by 2030. But as it edges the United States closer to achieving its international climate commitment, the legislation is also expected to yield significant — and more immediate — improvements in the nation’s health. If successful in accelerating the transition from fossil fuels to clean energy alternatives, the IRA will sharply reduce atmospheric concentrations of fine particulates known to exacerbate respiratory and cardiovascular disease and cause premature deaths, along with other air pollutants that degrade human health. One recent study shows that eliminating air pollution from fossil fuels in the contiguous United States would prevent more than 50,000 premature deaths and avoid more than $600 billion in health costs each year.

      While national climate policies such as those advanced by the IRA can simultaneously help mitigate climate change and improve air quality, their results may vary widely when it comes to improving public health. That’s because the potential health benefits associated with air quality improvements are much greater in some regions and economic sectors than in others. Those benefits can be maximized, however, through a prudent combination of climate and air-quality policies.

      Several past studies have evaluated the likely health impacts of various policy combinations, but their usefulness has been limited due to a reliance on a small set of standard policy scenarios. More versatile tools are needed to model a wide range of climate and air-quality policy combinations and assess their collective effects on air quality and human health. Now researchers at the MIT Joint Program on the Science and Policy of Global Change and MIT Institute for Data, Systems and Society (IDSS) have developed a publicly available, flexible scenario tool that does just that.

      In a study published in the journal Geoscientific Model Development, the MIT team introduces its Tool for Air Pollution Scenarios (TAPS), which can be used to estimate the likely air-quality and health outcomes of a wide range of climate and air-quality policies at the regional, sectoral, and fuel-based level. 

      “This tool can help integrate the siloed sustainability issues of air pollution and climate action,” says the study’s lead author William Atkinson, who recently served as a Biogen Graduate Fellow and research assistant at the IDSS Technology and Policy Program’s (TPP) Research to Policy Engagement Initiative. “Climate action does not guarantee a clean air future, and vice versa — but the issues have similar sources that imply shared solutions if done right.”

      The study’s initial application of TAPS shows that with current air-quality policies and near-term Paris Agreement climate pledges alone, short-term pollution reductions give way to long-term increases — given the expected growth of emissions-intensive industrial and agricultural processes in developing regions. More ambitious climate and air-quality policies could be complementary, each reducing different pollutants substantially to give tremendous near- and long-term health benefits worldwide.

      “The significance of this work is that we can more confidently identify the long-term emission reduction strategies that also support air quality improvements,” says MIT Joint Program Deputy Director C. Adam Schlosser, a co-author of the study. “This is a win-win for setting climate targets that are also healthy targets.”

      TAPS projects air quality and health outcomes based on three integrated components: a recent global inventory of detailed emissions resulting from human activities (e.g., fossil fuel combustion, land-use change, industrial processes); multiple scenarios of emissions-generating human activities between now and the year 2100, produced by the MIT Economic Projection and Policy Analysis model; and emissions intensity (emissions per unit of activity) scenarios based on recent data from the Greenhouse Gas and Air Pollution Interactions and Synergies model.

      “We see the climate crisis as a health crisis, and believe that evidence-based approaches are key to making the most of this historic investment in the future, particularly for vulnerable communities,” says Johanna Jobin, global head of corporate reputation and responsibility at Biogen. “The scientific community has spoken with unanimity and alarm that not all climate-related actions deliver equal health benefits. We’re proud of our collaboration with the MIT Joint Program to develop this tool that can be used to bridge research-to-policy gaps, support policy decisions to promote health among vulnerable communities, and train the next generation of scientists and leaders for far-reaching impact.”

      The tool can inform decision-makers about a wide range of climate and air-quality policies. Policy scenarios can be applied to specific regions, sectors, or fuels to investigate policy combinations at a more granular level, or to target short-term actions with high-impact benefits.

      TAPS could be further developed to account for additional emissions sources and trends.

      “Our new tool could be used to examine a large range of both climate and air quality scenarios. As the framework is expanded, we can add detail for specific regions, as well as additional pollutants such as air toxics,” says study supervising co-author Noelle Selin, professor at IDSS and the MIT Department of Earth, Atmospheric and Planetary Sciences, and director of TPP.    

      This research was supported by the U.S. Environmental Protection Agency and its Science to Achieve Results (STAR) program; Biogen; TPP’s Leading Technology and Policy Initiative; and TPP’s Research to Policy Engagement Initiative. More

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      “Drawing Together” is awarded Norman B. Leventhal City Prize

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      “Drawing Together,” a social and ecological resilience project in New York City, has been awarded the 2022 Norman B. Leventhal City Prize. 

      The project is a collaboration between MIT faculty, researchers, and students, and Green City Force (GCF), a nonprofit organization in New York City that trains young people for careers with a sustainability focus while they serve local public housing communities.

      The winning proposal was submitted by a team led by MIT’s Miho Mazereeuw, associate professor and director of the Urban Risk Lab; Nicholas de Monchaux, professor and head of the Department of Architecture; Carlos Sandoval Olascoaga PhD ’21, a postdoc in the Department of Architecture and the MIT Schwarzman College of Computing; and Tonya Gayle, executive director of Green City Force.

      Through their Service Corps (affiliated with the national AmeriCorps service and training program), GCF trains young residents of New York City Housing Authority public housing to participate in large-scale environmental and health initiatives in public housing and other local communities.

      The Drawing Together team will collaborate with GCF on its “Eco-Hubs,” an urban farms initiative. In a co-design effort, Drawing Together will create a new digital platform to support community-led planning and design processes for the siting, design, and operation of these spaces. This platform will also facilitate the scaling-up of community engagement with Eco-Hubs.

      The $100,000 triennial prize was established in 2019 by MIT’s Norman B. Leventhal Center for Advanced Urbanism (LCAU) to catalyze innovative interdisciplinary urban design and planning approaches worldwide to improve the environment as well as the quality of life for residents. The first awardee was “Malden River Works for Waterfront Equity and Resilience,” a project for a civic waterfront space in Malden, Massachusetts.

      The 2022 Leventhal City Prize call for submissions sought proposals that focused on digital urbanism — investigating how life in cities can be improved using digital tools that are equitable and responsive to social and environmental conditions. The jury reviewed proposals for projects that offered new urban design and planning solutions using evolving data sources and computational techniques that transform the quality of life in metropolitan environments.

      “Digital urbanism is the intersection between cities, design, and technology and how we can identify new ways to include technology and design in our cities,” says LCAU Director Sarah Williams. “Drawing Together perfectly exemplifies how digital urbanism can assist in the co-development of design solution and improve the quality of life for the public.”

      The team will expand the workforce training currently offered by GCF to incorporate digital skills, with the goal of developing and integrating a sustainability-focused data science curriculum that supports sustainable urban farming within the Eco-Hubs.

      “What is most inspiring about this project is that young people are the writers, rather than passive subjects of urban transformation,” says juror Garrett Dash Nelson, president and head curator of the Norman B. Leventhal Map and Education Center at the Boston Public Library. “By taking the information and design architectures and making them central to youth-driven decisions about environmental planning, this project has the potential to activate a new participatory paradigm that will resonate far beyond New York City.”

      “In addition to community-based digital methods for urban environmental design, this project has the potential to strengthen computational skills in green job opportunities for youth that the Green City Force Eco-Hubs serve,” says juror James Wescoat, MIT Aga Khan Professor Emeritus of Landscape Architecture and Geography. 

      In addition to Nelson and Wescoat, the jury for this year’s competition included Lilian Coral, director of National Strategy and Technology Innovation at the Knight Foundation; Jose Castillo, principal at a|911 and professor of urbanism at CENTRO University; and Nigel Jacob, senior fellow at the Burnes Center for Global Impact at Northeastern University.

      The prize jury identified two finalists. Co-HATY Accelerator Team is a multidisciplinary project that helps provide housing and social support to Ukraine’s displaced residents. The team of urban planners, information technologists, architects, and sociologists are using digital technology to better connect residents across the country with housing opportunities. Team members include Brent D. Ryan, associate professor of urban design and public policy at MIT, and Anastasiya Ponomaryova, urban designer and co-founder of co-HATY.

      “The Ukraine’s team proposal makes a point of the relevance of architecture and planning in the context of humanitarian crises,” says Castillo. “It forces us to deploy techniques, methods, and knowledge to resolve issues ‘on demand.’ Different from a view of architecture and planning as ’slow practices,’ where design processes, research, pedagogies, and buildings take a long time to be deployed and finalized, this research shows an agile but thorough approach to the immediate and the contingent.”

      The second finalist is “Ozymandias: Using Artificial Intelligence to Map Urban Power Structures and Produce Fairer Results for All,” a project led by the Portland, Maine, Society for Architecture. The team behind this project seeks to encourage broader civic participation and positive change in municipal governments. By using emerging AI computation tools to illuminate patterns in power structures and decision-making, the team hopes to highlight correctable yet previously unrecognizable inequities. Principal investigator for the project is Jeff Levine, a lecturer in MIT’s Department of Urban Studies and Planning and a past director of planning and urban development for Portland.

      “The Ozymandias project recognizes an important truth about urban decision-making — that it is neither a bottom-up nor a top-down structure, but a tangled and often obscure network of formal and informal power systems,” says Nelson. “By bringing analytical methods to bear on a perennial question for civic action — who really governs in a democratic system? — the project offers a provocative methodology for examining why nominally participatory urban processes so often fail at producing inclusive and equitable outcomes.” More

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      Scientists chart how exercise affects the body

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      Exercise is well-known to help people lose weight and avoid gaining it. However, identifying the cellular mechanisms that underlie this process has proven difficult because so many cells and tissues are involved.

      In a new study in mice that expands researchers’ understanding of how exercise and diet affect the body, MIT and Harvard Medical School researchers have mapped out many of the cells, genes, and cellular pathways that are modified by exercise or high-fat diet. The findings could offer potential targets for drugs that could help to enhance or mimic the benefits of exercise, the researchers say.

      “It is extremely important to understand the molecular mechanisms that are drivers of the beneficial effects of exercise and the detrimental effects of a high-fat diet, so that we can understand how we can intervene, and develop drugs that mimic the impact of exercise across multiple tissues,” says Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard.

      The researchers studied mice with high-fat or normal diets, who were either sedentary or given the opportunity to exercise whenever they wanted. Using single-cell RNA sequencing, the researchers cataloged the responses of 53 types of cells found in skeletal muscle and two types of fatty tissue.

      “One of the general points that we found in our study, which is overwhelmingly clear, is how high-fat diets push all of these cells and systems in one way, and exercise seems to be pushing them nearly all in the opposite way,” Kellis says. “It says that exercise can really have a major effect throughout the body.”

      Kellis and Laurie Goodyear, a professor of medicine at Harvard Medical School and senior investigator at the Joslin Diabetes Center, are the senior authors of the study, which appears today in the journal Cell Metabolism. Jiekun Yang, a research scientist in MIT CSAIL; Maria Vamvini, an instructor of medicine at the Joslin Diabetes Center; and Pasquale Nigro, an instructor of medicine at the Joslin Diabetes Center, are the lead authors of the paper.

      The risks of obesity

      Obesity is a growing health problem around the world. In the United States, more than 40 percent of the population is considered obese, and nearly 75 percent is overweight. Being overweight is a risk factor for many diseases, including heart disease, cancer, Alzheimer’s disease, and even infectious diseases such as Covid-19.

      “Obesity, along with aging, is a global factor that contributes to every aspect of human health,” Kellis says.

      Several years ago, his lab performed a study on the FTO gene region, which has been strongly linked to obesity risk. In that 2015 study, the research team found that genes in this region control a pathway that prompts immature fat cells called progenitor adipocytes to either become fat-burning cells or fat-storing cells.

      That finding, which demonstrated a clear genetic component to obesity, motivated Kellis to begin looking at how exercise, a well-known behavioral intervention that can prevent obesity, might act on progenitor adipocytes at the cellular level.

      To explore that question, Kellis and his colleagues decided to perform single-cell RNA sequencing of three types of tissue — skeletal muscle, visceral white adipose tissue (found packed around internal organs, where it stores fat), and subcutaneous white adipose tissue (which is found under the skin and primarily burns fat).

      These tissues came from mice from four different experimental groups. For three weeks, two groups of mice were fed either a normal diet or a high-fat diet. For the next three weeks, each of those two groups were further divided into a sedentary group and an exercise group, which had continuous access to a treadmill.

      By analyzing tissues from those mice, the researchers were able to comprehensively catalog the genes that were activated or suppressed by exercise in 53 different cell types.

      The researchers found that in all three tissue types, mesenchymal stem cells (MSCs) appeared to control many of the diet and exercise-induced effects that they observed. MSCs are stem cells that can differentiate into other cell types, including fat cells and fibroblasts. In adipose tissue, the researchers found that a high-fat diet modulated MSCs’ capacity to differentiate into fat-storing cells, while exercise reversed this effect.

      In addition to promoting fat storage, the researchers found that a high-fat diet also stimulated MSCs to secrete factors that remodel the extracellular matrix (ECM) — a network of proteins and other molecules that surround and support cells and tissues in the body. This ECM remodeling helps provide structure for enlarged fat-storing cells and also creates a more inflammatory environment.

      “As the adipocytes become overloaded with lipids, there’s an extreme amount of stress, and that causes low-grade inflammation, which is systemic and preserved for a long time,” Kellis says. “That is one of the factors that is contributing to many of the adverse effects of obesity.”

      Circadian effects

      The researchers also found that high-fat diets and exercise had opposing effects on cellular pathways that control circadian rhythms — the 24-hour cycles that govern many functions, from sleep to body temperature, hormone release, and digestion. The study revealed that exercise boosts the expression of genes that regulate these rhythms, while a high-fat diet suppresses them.

      “There have been a lot of studies showing that when you eat during the day is extremely important in how you absorb the calories,” Kellis says. “The circadian rhythm connection is a very important one, and shows how obesity and exercise are in fact directly impacting that circadian rhythm in peripheral organs, which could act systemically on distal clocks and regulate stem cell functions and immunity.”

      The researchers then compared their results to a database of human genes that have been linked with metabolic traits. They found that two of the circadian rhythm genes they identified in this study, known as DBP and CDKN1A, have genetic variants that have been associated with a higher risk of obesity in humans.

      “These results help us see the translational values of these targets, and how we could potentially target specific biological processes in specific cell types,” Yang says.

      The researchers are now analyzing samples of small intestine, liver, and brain tissue from the mice in this study, to explore the effects of exercise and high-fat diets on those tissues. They are also conducting work with human volunteers to sample blood and biopsies and study similarities and differences between human and mouse physiology. They hope that their findings will help guide drug developers in designing drugs that might mimic some of the beneficial effects of exercise.

      “The message for everyone should be, eat a healthy diet and exercise if possible,” Kellis says. “For those for whom this is not possible, due to low access to healthy foods, or due to disabilities or other factors that prevent exercise, or simply lack of time to have a healthy diet or a healthy lifestyle, what this study says is that we now have a better handle on the pathways, the specific genes, and the specific molecular and cellular processes that we should be manipulating therapeutically.”

      The research was funded by the National Institutes of Health and the Novo Nordisk Research Center in Seattle. More

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      Computing for the health of the planet

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      The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

      Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

      The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

      The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty — 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

      Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

      “By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.”

      “These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The college plays a pivotal role in MIT’s overarching effort to hire climate-focused faculty — introducing the critical role of computing to address the health of the planet through innovative research and curriculum.”

      The four new faculty members are:

      Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023. Beery received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

      Priya Donti will join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24. Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

      Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

      Sherrie Wang will join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell. More

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      in Energy

      Taking a magnifying glass to data center operations

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      When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner — and the team is looking for ways to improve.

      “We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC. 

      To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.

      Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow. They also see potential for leveraging AI in the data center itself, by using the data to develop models for predicting failure points, optimizing job scheduling, and improving energy efficiency. While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage. The release of this dataset and associated code seeks to fill this space.

      “Data centers are changing. We have an explosion of hardware platforms, the types of workloads are evolving, and the types of people who are using data centers is changing,” says Vijay Gadepally, a senior researcher at the LLSC. “Until now, there hasn’t been a great way to analyze the impact to data centers. We see this research and dataset as a big step toward coming up with a principled approach to understanding how these variables interact with each other and then applying AI for insights and improvements.”

      Papers describing the dataset and potential applications have been accepted to a number of venues, including the IEEE International Symposium on High-Performance Computer Architecture, the IEEE International Parallel and Distributed Processing Symposium, the Annual Conference of the North American Chapter of the Association for Computational Linguistics, the IEEE High-Performance and Embedded Computing Conference, and International Conference for High Performance Computing, Networking, Storage and Analysis. 

      Workload classification

      Among the world’s TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accelerators. These NVIDIA GPUs are specialized for deep learning, the class of AI that has given rise to speech recognition and computer vision.

      The dataset covers CPU, GPU, and memory usage by job; scheduling logs; and physical monitoring data. Compared to similar datasets, such as those from Google and Microsoft, the LLSC dataset offers “labeled data, a variety of known AI workloads, and more detailed time series data compared with prior datasets. To our knowledge, it’s one of the most comprehensive and fine-grained datasets available,” Gadepally says. 

      Notably, the team collected time-series data at an unprecedented level of detail: 100-millisecond intervals on every GPU and 10-second intervals on every CPU, as the machines processed more than 3,000 known deep-learning jobs. One of the first goals is to use this labeled dataset to characterize the workloads that different types of deep-learning jobs place on the system. This process would extract features that reveal differences in how the hardware processes natural language models versus image classification or materials design models, for example.   

      The team has now launched the MIT Datacenter Challenge to mobilize this research. The challenge invites researchers to use AI techniques to identify with 95 percent accuracy the type of job that was run, using their labeled time-series data as ground truth.

      Such insights could enable data centers to better match a user’s job request with the hardware best suited for it, potentially conserving energy and improving system performance. Classifying workloads could also allow operators to quickly notice discrepancies resulting from hardware failures, inefficient data access patterns, or unauthorized usage.

      Too many choices

      Today, the LLSC offers tools that let users submit their job and select the processors they want to use, “but it’s a lot of guesswork on the part of users,” Samsi says. “Somebody might want to use the latest GPU, but maybe their computation doesn’t actually need it and they could get just as impressive results on CPUs, or lower-powered machines.”

      Professor Devesh Tiwari at Northeastern University is working with the LLSC team to develop techniques that can help users match their workloads to appropriate hardware. Tiwari explains that the emergence of different types of AI accelerators, GPUs, and CPUs has left users suffering from too many choices. Without the right tools to take advantage of this heterogeneity, they are missing out on the benefits: better performance, lower costs, and greater productivity.

      “We are fixing this very capability gap — making users more productive and helping users do science better and faster without worrying about managing heterogeneous hardware,” says Tiwari. “My PhD student, Baolin Li, is building new capabilities and tools to help HPC users leverage heterogeneity near-optimally without user intervention, using techniques grounded in Bayesian optimization and other learning-based optimization methods. But, this is just the beginning. We are looking into ways to introduce heterogeneity in our data centers in a principled approach to help our users achieve the maximum advantage of heterogeneity autonomously and cost-effectively.”

      Workload classification is the first of many problems to be posed through the Datacenter Challenge. Others include developing AI techniques to predict job failures, conserve energy, or create job scheduling approaches that improve data center cooling efficiencies.

      Energy conservation 

      To mobilize research into greener computing, the team is also planning to release an environmental dataset of TX-GAIA operations, containing rack temperature, power consumption, and other relevant data.

      According to the researchers, huge opportunities exist to improve the power efficiency of HPC systems being used for AI processing. As one example, recent work in the LLSC determined that simple hardware tuning, such as limiting the amount of power an individual GPU can draw, could reduce the energy cost of training an AI model by 20 percent, with only modest increases in computing time. “This reduction translates to approximately an entire week’s worth of household energy for a mere three-hour time increase,” Gadepally says.

      They have also been developing techniques to predict model accuracy, so that users can quickly terminate experiments that are unlikely to yield meaningful results, saving energy. The Datacenter Challenge will share relevant data to enable researchers to explore other opportunities to conserve energy.

      The team expects that lessons learned from this research can be applied to the thousands of data centers operated by the U.S. Department of Defense. The U.S. Air Force is a sponsor of this work, which is being conducted under the USAF-MIT AI Accelerator.

      Other collaborators include researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Charles Leiserson’s Supertech Research Group is investigating performance-enhancing techniques for parallel computing, and research scientist Neil Thompson is designing studies on ways to nudge data center users toward climate-friendly behavior.

      Samsi presented this work at the inaugural AI for Datacenter Optimization (ADOPT’22) workshop last spring as part of the IEEE International Parallel and Distributed Processing Symposium. The workshop officially introduced their Datacenter Challenge to the HPC community.

      “We hope this research will allow us and others who run supercomputing centers to be more responsive to user needs while also reducing the energy consumption at the center level,” Samsi says. More

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      in Energy

      New hardware offers faster computation for artificial intelligence, with much less energy

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      As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

      Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

      A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

      Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

      “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

      “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

      “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

      These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

      “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

      Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

      Accelerating deep learning

      Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

      The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

      In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

      The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

      To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

      PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

      Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

      Surprising speed

      PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

      “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

      “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

      Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

      Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

      Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

      At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

      “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

      “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

      “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

      “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

      This research is funded, in part, by the MIT-IBM Watson AI Lab. More