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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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      The science of strength: How data analytics is transforming college basketball

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      In the 1990s, if you suggested that the corner three-pointer was the best shot in basketball, you might have been laughed out of the gym.

      The game was still dominated largely by a fleet of seven-foot centers, most of whom couldn’t shoot from more than a few feet out from the basket. Even the game’s best player, Michael Jordan, was a mid-range specialist who averaged under two three-point attempts per game for his career.

      Fast forward to today, and the best players average around a dozen long-ball attempts per game — typically favoring shots from the corner.

      What’s changed? Analytics.

      “When I first started in the profession, 10 to 12 years ago, data analytics was almost nonexistent in training rooms,” says Adam Petway, the director of strength and conditioning for men’s basketball at the University of Louisville. “Today, we have force platform technology, we have velocity-based training, we have GPS tracking during games and in training, all to get a more objective analysis to help our athletes. So it’s grown exponentially.”

      Petway, who previously worked on the coaching staffs of the NBA’s Philadelphia 76ers and Washington Wizards, holds a bachelor’s degree in sports science, an MBA with an emphasis in sport management, and a doctorate in sports science. Recently, he extended his education through MIT Professional Education’s Applied Data Science Program (ADSP).

      “The impetus behind enrolling in ADSP was primarily a curiosity to learn and a desire to get better,” Petway says. “In my time in pro and college sports, we’ve had whole departments dedicated to data science, so I know it’s a skill set I’ll need in the future.”

      Applying new skills

      Petway took classes in a live online format. Although he was the only strength and conditioning coach in his cohort — learning alongside lawyers, professors, and business executives — he says that the focus on data gave all of his classmates a common language of sorts.

      “In many people’s minds, the worlds of data science and NCAA strength and conditioning training may not cross. We are finding that there are many other professional and industry sectors that can benefit from data science and analytics, which explains why we are seeing an ever-growing range of professionals from around the globe enroll in our Applied Data Science Program,” says Bhaskar Pant, executive director of MIT Professional Education. “It’s exciting to hear how change-makers like Adam are using the knowledge they gained from the program to tackle their most pressing challenges using data science tools.”

      “Having access to such high-level practitioners within data science was something that I found very, very helpful,” Petway says. “The chance to interact with my classmates, and the chance to interact in small groups with the professionals and the professors, was unbelievable. When you’re writing code in Python you might mess up a semicolon and a comma, and get 200 characters into the code and realize that it’s not going to work. So the ability to stop and ask questions, and really get into the material with a cohort of peers from different industries, that was really helpful.”

      Petway points to his newfound abilities to code in Python, and to run data through artificial intelligence programs that utilize unsupervised learning techniques, as major takeaways from his experience. Sports teams produce a wealth of data, he notes, but coaches need to be able to process that information in ways that lead to actionable insights.

      “Now I’m able to create decision trees, do visualization with data, and run a principal component analysis,” Petway says. “So instead of relying on third-party companies to come in and tell me what to do, I can take all of that data and disseminate the results myself, which not only saves me time, but it saves a lot of money.”

      In addition to giving him new capabilities in his coaching role, the skills were crucial to the research for a paper that Petway and a team of several other authors published in the International Journal of Strength and Conditioning this year. “The data came from my PhD program around five years ago,” Petway notes. “I had the data already, but I couldn’t properly visualize it and analyze it until I took the MIT Professional Education course.”

      “MIT’s motto is ‘mens et manus’ (‘mind and hand’), which translates to experience-based learning. As such, there was great thought put into how the Applied Data Science Program is structured. The expectation is that every participant not only gains foundational skills, but also learns how to apply that knowledge in real-world scenarios. We are thrilled to see learning from our course applied to top-level college basketball,” says Munther Dahleh, director of the Institute for Data, Systems, and Society, the William A. Coolidge Professor of Electrical Engineering and Computer Science at MIT, and one of the instructors of ADSP.

      Data’s growing role in sports

      Analytics are pushing the field of strength and conditioning far beyond the days when trainers would simply tell players to do a certain number of reps in the weight room, Petway says. Wearable devices help to track how much ground athletes cover during practice, as well as their average speed. Data from a force platform helps Petway to analyze the force with which basketball players jump (and land), and even to determine how much force an athlete is generating from each leg. Using a tool called a linear position transducer, Petway can measure how fast athletes are moving a prescribed load during weight-lifting exercises.

      “Instead of telling someone to do 90 percent of their squat max, we’re telling them to squat 200 kilos, and to move it at a rate above one meter per second,” says Petway. “So it’s more power- and velocity-driven than your traditional weight training.”

      The goal is to not only improve athlete’s performance, Petway says, but also to create training programs that minimize the chance of injury. Sometimes, that means deviating from well-worn sports cliches about “giving 110 percent” or “leaving it all on the court.”

      “There’s a misconception that doing more is always better,” Petway says. “One of my mentors would always say, ‘Sometimes you have to have the courage to do less.’ The most important thing for our athletes is being available for competition. We can use data analytics now to forecast the early onset of fatigue. If we see that their power output in the weight room is decreasing, we may need to intervene with rest before things get worse. It’s about using information to make more objective decisions.”

      The ability to create visuals from data, Petway says, has greatly enhanced his ability to communicate with athletes and other coaches about what he’s seeing in the numbers. “It’s a really powerful tool, being able to take a bunch of data points and show that things are trending up or down, along with the intervention we’re going to need to make based on what the data suggests,” he says.

      Ultimately, Petway notes, coaches are primarily interested in just one data point: wins and losses. But as more sports professionals see that data science can lead to more wins, he says, analytics will continue to gain a foothold in the industry. “If you can show that preparing a certain way leads to a higher likelihood that the team will win, that really speaks coaches’ language,” he says. “They just want to see results. And if data science can help deliver those results, they’re going to be bought in.” More

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      Four from MIT receive NIH New Innovator Awards for 2022

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      The National Institutes of Health (NIH) has awarded grants to four MIT faculty members as part of its High-Risk, High-Reward Research program.

      The program supports unconventional approaches to challenges in biomedical, behavioral, and social sciences. Each year, NIH Director’s Awards are granted to program applicants who propose high-risk, high-impact research in areas relevant to the NIH’s mission. In doing so, the NIH encourages innovative proposals that, due to their inherent risk, might struggle in the traditional peer-review process.

      This year, Lindsay Case, Siniša Hrvatin, Deblina Sarkar, and Caroline Uhler have been chosen to receive the New Innovator Award, which funds exceptionally creative research from early-career investigators. The award, which was established in 2007, supports researchers who are within 10 years of their final degree or clinical residency and have not yet received a research project grant or equivalent NIH grant.

      Lindsay Case, the Irwin and Helen Sizer Department of Biology Career Development Professor and an extramural member of the Koch Institute for Integrative Cancer Research, uses biochemistry and cell biology to study the spatial organization of signal transduction. Her work focuses on understanding how signaling molecules assemble into compartments with unique biochemical and biophysical properties to enable cells to sense and respond to information in their environment. Earlier this year, Case was one of two MIT assistant professors named as Searle Scholars.

      Siniša Hrvatin, who joined the School of Science faculty this past winter, is an assistant professor in the Department of Biology and a core member at the Whitehead Institute for Biomedical Research. He studies how animals and cells enter, regulate, and survive states of dormancy such as torpor and hibernation, aiming to harness the potential of these states therapeutically.

      Deblina Sarkar is an assistant professor and AT&T Career Development Chair Professor at the MIT Media Lab​. Her research combines the interdisciplinary fields of nanoelectronics, applied physics, and biology to invent disruptive technologies for energy-efficient nanoelectronics and merge such next-generation technologies with living matter to create a new paradigm for life-machine symbiosis. Her high-risk, high-reward proposal received the rare perfect impact score of 10, which is the highest score awarded by NIH.

      Caroline Uhler is a professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. In addition, she is a core institute member at the Broad Institute of MIT and Harvard, where she co-directs the Eric and Wendy Schmidt Center. By combining machine learning, statistics, and genomics, she develops representation learning and causal inference methods to elucidate gene regulation in health and disease.

      The High-Risk, High-Reward Research program is supported by the NIH Common Fund, which oversees programs that pursue major opportunities and gaps in biomedical research that require collaboration across NIH Institutes and Centers. In addition to the New Innovator Award, the NIH also issues three other awards each year: the Pioneer Award, which supports bold and innovative research projects with unusually broad scientific impact; the Transformative Research Award, which supports risky and untested projects with transformative potential; and the Early Independence Award, which allows especially impressive junior scientists to skip the traditional postdoctoral training program to launch independent research careers.

      This year, the High-Risk, High-Reward Research program is awarding 103 awards, including eight Pioneer Awards, 72 New Innovator Awards, nine Transformative Research Awards, and 14 Early Independence Awards. These 103 awards total approximately $285 million in support from the institutes, centers, and offices across NIH over five years. “The science advanced by these researchers is poised to blaze new paths of discovery in human health,” says Lawrence A. Tabak DDS, PhD, who is performing the duties of the director of NIH. “This unique cohort of scientists will transform what is known in the biological and behavioral world. We are privileged to support this innovative science.” More

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      Investigating at the interface of data science and computing

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      A visual model of Guy Bresler’s research would probably look something like a Venn diagram. He works at the four-way intersection where theoretical computer science, statistics, probability, and information theory collide.

      “There are always new things to do be done at the interface. There are always opportunities for entirely new questions to ask,” says Bresler, an associate professor who recently earned tenure in MIT’s Department of Electrical Engineering and Computer Science (EECS).

      A theoretician, he aims to understand the delicate interplay between structure in data, the complexity of models, and the amount of computation needed to learn those models. Recently, his biggest focus has been trying to unveil fundamental phenomena that are broadly responsible for determining the computational complexity of statistics problems — and finding the “sweet spot” where available data and computation resources enable researchers to effectively solve a problem.

      When trying to solve a complex statistics problem, there is often a tug-of-war between data and computation. Without enough data, the computation needed to solve a statistical problem can be intractable, or at least consume a staggering amount of resources. But get just enough data and suddenly the intractable becomes solvable; the amount of computation needed to come up with a solution drops dramatically.

      The majority of modern statistical problems exhibits this sort of trade-off between computation and data, with applications ranging from drug development to weather prediction. Another well-studied and practically important example is cryo-electron microscopy, Bresler says. With this technique, researchers use an electron microscope to take images of molecules in different orientations. The central challenge is how to solve the inverse problem — determining the molecule’s structure given the noisy data. Many statistical problems can be formulated as inverse problems of this sort.

      One aim of Bresler’s work is to elucidate relationships between the wide variety of different statistics problems currently being studied. The dream is to classify statistical problems into equivalence classes, as has been done for other types of computational problems in the field of computational complexity. Showing these sorts of relationships means that, instead of trying to understand each problem in isolation, researchers can transfer their understanding from a well-studied problem to a poorly understood one, he says.

      Adopting a theoretical approach

      For Bresler, a desire to theoretically understand various basic phenomena inspired him to follow a path into academia.

      Both of his parents worked as professors and showed how fulfilling academia can be, he says. His earliest introduction to the theoretical side of engineering came from his father, who is an electrical engineer and theoretician studying signal processing. Bresler was inspired by his work from an early age. As an undergraduate at the University of Illinois at Urbana-Champaign, he bounced between physics, math, and computer science courses. But no matter the topic, he gravitated toward the theoretical viewpoint.

      In graduate school at the University of California at Berkeley, Bresler enjoyed the opportunity to work in a wide variety of topics spanning probability, theoretical computer science, and mathematics. His driving motivator was a love of learning new things.

      “Working at the interface of multiple fields with new questions, there is a feeling that one had better learn as much as possible if one is to have any chance of finding the right tools to answer those questions,” he says.

      That curiosity led him to MIT for a postdoc in the Laboratory for Information and Decision Systems (LIDS) in 2013, and then he joined the faculty two years later as an assistant professor in EECS. He was named an associate professor in 2019.

      Bresler says he was drawn to the intellectual atmosphere at MIT, as well as the supportive environment for launching bold research quests and trying to make progress in new areas of study.

      Opportunities for collaboration

      “What really struck me was how vibrant and energetic and collaborative MIT is. I have this mental list of more than 20 people here who I would love to have lunch with every single week and collaborate with on research. So just based on sheer numbers, joining MIT was a clear win,” he says.

      He’s especially enjoyed collaborating with his students, who continually teach him new things and ask deep questions that drive exciting research projects. One such student, Matthew Brennan, who was one of Bresler’s closest collaborators, tragically and unexpectedly passed away in January, 2021.

      The shock from Brennan’s death is still raw for Bresler, and it derailed his research for a time.

      “Beyond his own prodigious capabilities and creativity, he had this amazing ability to listen to an idea of mine that was almost completely wrong, extract from it a useful piece, and then pass the ball back,” he says. “We had the same vision for what we wanted to achieve in the work, and we were driven to try to tell a certain story. At the time, almost nobody was pursuing this particular line of work, and it was in a way kind of lonely. But he trusted me, and we encouraged one another to keep at it when things seemed bleak.”

      Those lessons in perseverance fuel Bresler as he and his students continue exploring questions that, by their nature, are difficult to answer.

      One area he’s worked in on-and-off for over a decade involves learning graphical models from data. Models of certain types of data, such as time-series data consisting of temperature readings, are often constructed by domain experts who have relevant knowledge and can build a reasonable model, he explains.

      But for many types of data with complex dependencies, such as social network or biological data, it is not at all clear what structure a model should take. Bresler’s work seeks to estimate a structured model from data, which could then be used for downstream applications like making recommendations or better predicting the weather.

      The basic question of identifying good models, whether algorithmically in a complex setting or analytically, by specifying a useful toy model for theoretical analysis, connects the abstract work with engineering practice, he says.

      “In general, modeling is an art. Real life is complicated and if you write down some super-complicated model that tries to capture every feature of a problem, it is doomed,” says Bresler. “You have to think about the problem and understand the practical side of things on some level to identify the correct features of the problem to be modeled, so that you can hope to actually solve it and gain insight into what one should do in practice.”

      Outside the lab, Bresler often finds himself solving very different kinds of problems. He is an avid rock climber and spends much of his free time bouldering throughout New England.

      “I really love it. It is a good excuse to get outside and get sucked into a whole different world. Even though there is problem solving involved, and there are similarities at the philosophical level, it is totally orthogonal to sitting down and doing math,” he says. More

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      New program to support translational research in AI, data science, and machine learning

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      The MIT School of Engineering and Pillar VC today announced the MIT-Pillar AI Collective, a one-year pilot program funded by a gift from Pillar VC that will provide seed grants for projects in artificial intelligence, machine learning, and data science with the goal of supporting translational research. The program will support graduate students and postdocs through access to funding, mentorship, and customer discovery.

      Administered by the MIT Deshpande Center for Technological Innovation, the MIT-Pillar AI Collective will center on the market discovery process, advancing projects through market research, customer discovery, and prototyping. Graduate students and postdocs will aim to emerge from the program having built minimum viable products, with support from Pillar VC and experienced industry leaders.

      “We are grateful for this support from Pillar VC and to join forces to converge the commercialization of translational research in AI, data science, and machine learning, with an emphasis on identifying and cultivating prospective entrepreneurs,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Pillar’s focus on mentorship for our graduate students and postdoctoral researchers, and centering the program within the Deshpande Center, will undoubtedly foster big ideas in AI and create an environment for prospective companies to launch and thrive.” 

      Founded by Jamie Goldstein ’89, Pillar VC is committed to growing companies and investing in personal and professional development, coaching, and community.

      “Many of the most promising companies of the future are living at MIT in the form of transformational research in the fields of data science, AI, and machine learning,” says Goldstein. “We’re honored by the chance to help unlock this potential and catalyze a new generation of founders by surrounding students and postdoctoral researchers with the resources and mentorship they need to move from the lab to industry.”

      The program will launch with the 2022-23 academic year. Grants will be open only to MIT faculty and students, with an emphasis on funding for graduate students in their final year, as well as postdocs. Applications must be submitted by MIT employees with principal investigator status. A selection committee composed of three MIT representatives will include Devavrat Shah, faculty director of the Deshpande Center, the Andrew (1956) and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society; the chair of the selection committee; and a representative from the MIT Schwarzman College of Computing. The committee will also include representation from Pillar VC. Funding will be provided for up to nine research teams.

      “The Deshpande Center will serve as the perfect home for the new collective, given its focus on moving innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies,” adds Chandrakasan. 

      “The Deshpande Center has a 20-year history of guiding new technologies toward commercialization, where they can have a greater impact,” says Shah. “This new collective will help the center expand its own impact by helping more projects realize their market potential and providing more support to researchers in the fast-growing fields of AI, machine learning, and data science.” 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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      New leadership at MIT’s Center for Biomedical Innovation

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      As it continues in its mission to improve global health through the development and implementation of biomedical innovation, the MIT Center for Biomedical Innovation (CBI) today announced changes to its leadership team: Stacy Springs has been named executive director, and Professor Richard Braatz has joined as the center’s new associate faculty director.

      The change in leadership comes at a time of rapid development in new therapeutic modalities, growing concern over global access to biologic medicines and healthy food, and widespread interest in applying computational tools and multi-disciplinary approaches to address long-standing biomedical challenges.

      “This marks an exciting new chapter for the CBI,” says faculty director Anthony J. Sinskey, professor of biology, who cofounded CBI in 2005. “As I look back at almost 20 years of CBI history, I see an exponential growth in our activities, educational offerings, and impact.”

      The center’s collaborative research model accelerates innovation in biotechnology and biomedical research, drawing on the expertise of faculty and researchers in MIT’s schools of Engineering and Science, the MIT Schwarzman College of Computing, and the MIT Sloan School of Management.

      Springs steps into the role of executive director having previously served as senior director of programs for CBI and as executive director of CBI’s Biomanufacturing Program and its Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB). She succeeds Gigi Hirsch, who founded the NEW Drug Development ParadIGmS (NEWDIGS) Initiative at CBI in 2009. Hirsch and NEWDIGS have now moved to Tufts Medical Center, establishing a headquarters at the new Center for Biomedical System Design within the Institute for Clinical Research and Health Policy Studies there.

      Braatz, a chemical engineer whose work is informed by mathematical modeling and computational techniques, conducts research in process data analytics, design, and control of advanced manufacturing systems.

      “It’s been great to interact with faculty from across the Institute who have complementary expertise,” says Braatz, the Edwin R. Gilliland Professor in the Department of Chemical Engineering. “Participating in CBI’s workshops has led to fruitful partnerships with companies in tackling industry-wide challenges.”

      CBI is housed under the Institute for Data Systems and Society and, specifically, the Sociotechnical Systems Research Center in the MIT Schwarzman College of Computing. CBI is home to two biomanufacturing consortia: the CAACB and the Biomanufacturing Consortium (BioMAN). Through these precompetitive collaborations, CBI researchers work with biomanufacturers and regulators to advance shared interests in biomanufacturing.

      In addition, CBI researchers are engaged in several sponsored research programs focused on integrated continuous biomanufacturing capabilities for monoclonal antibodies and vaccines, analytical technologies to measure quality and safety attributes of a variety of biologics, including gene and cell therapies, and rapid-cycle development of virus-like particle vaccines for SARS-CoV-2.

      In another significant initiative, CBI researchers are applying data analytics strategies to biomanufacturing problems. “In our smart data analytics project, we are creating new decision support tools and algorithms for biomanufacturing process control and plant-level decision-making. Further, we are leveraging machine learning and natural language processing to improve post-market surveillance studies,” says Springs.

      CBI is also working on advanced manufacturing for cell and gene therapies, among other new modalities, and is a part of the Singapore-MIT Alliance for Research and Technology – Critical Analytics for Manufacturing Personalized-Medicine (SMART CAMP). SMART CAMP is an international research effort focused on developing the analytical tools and biological understanding of critical quality attributes that will enable the manufacture and delivery of improved cell therapies to patients.

      “This is a crucial time for biomanufacturing and for innovation across the health-care value chain. The collaborative efforts of MIT researchers and consortia members will drive fundamental discovery and inform much-needed progress in industry,” says MIT Vice President for Research Maria Zuber.

      “CBI has a track record of engaging with health-care ecosystem challenges. I am confident that under the new leadership, it will continue to inspire MIT, the United States, and the entire world to improve the health of all people,” adds Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. More

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      in Data Management & Statistics

      MIT welcomes eight MLK Visiting Professors and Scholars for 2022-23

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      From space traffic to virus evolution, community journalism to hip-hop, this year’s cohort in the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program will power an unprecedented range of intellectual pursuits during their time on the MIT campus. 

      “MIT is so fortunate to have this group of remarkable individuals join us,” says Institute Community and Equity Officer John Dozier. “They bring a range and depth of knowledge to share with our students and faculty, and we look forward to working with them to build a stronger sense of community across the Institute.”

      Since its inception in 1990, the MLK Scholars Program has hosted more than 135 visiting professors, practitioners, and intellectuals who enhance and enrich the MIT community through their engagement with students and faculty. The program, which honors the life and legacy of MLK by increasing the presence and recognizing the contributions of underrepresented scholars, is supported by the Office of the Provost with oversight from the Institute Community and Equity Office. 

      In spring 2022, MIT President Rafael Reif committed to MIT to adding two new positions in the MLK Visiting Scholars Program, including an expert in Native American studies. Those additional positions will be filled in the coming year.  

      The 2022-23 MLK Scholars:

      Daniel Auguste is an assistant professor in the Department of Sociology at Florida Atlantic University and is hosted by Roberto Fernandez in MIT Sloan School of Management. Auguste’s research interests include social inequalities in entrepreneurship development. During his visit, Auguste will study the impact of education debt burden and wealth inequality on business ownership and success, and how these consequences differ by race and ethnicity.

      Tawanna Dillahunt is an associate professor in the School of Information at the University of Michigan, where she also holds an appointment with the electrical engineering and computer science department. Catherine D’Ignazio in the Department of Urban Studies and Planning and Fotini Christia in the Institute for Data, Systems, and Society are her faculty hosts. Dillahunt’s scholarship focuses on equitable and inclusive computing. She identifies technological opportunities and implements tools to address and alleviate employment challenges faced by marginalized people. Dillahunt’s visiting appointment begins in September 2023.

      Javit Drake ’94 is a principal scientist in modeling and simulation and measurement sciences at Proctor & Gamble. His faculty host is Fikile Brushett in the Department of Chemical Engineering. An industry researcher with electrochemical energy expertise, Drake is a Course 10 (chemical engineering) alumnus, repeat lecturer, and research affiliate in the department. During his visit, he will continue to work with the Brushett Research Group to deepen his research and understanding of battery technologies while he innovates from those discoveries.

      Eunice Ferreira is an associate professor in the Department of Theater at Skidmore College and is hosted by Claire Conceison in Music and Theater Arts. This fall, Ferreira will teach “Black Theater Matters,” a course where students will explore performance and the cultural production of Black intellectuals and artists on Broadway and in local communities. Her upcoming book projects include “Applied Theatre and Racial Justice: Radical Imaginings for Just Communities” (forthcoming from Routledge) and “Crioulo Performance: Remapping Creole and Mixed Race Theatre” (forthcoming from Vanderbilt University Press). 

      Wasalu Jaco, widely known as Lupe Fiasco, is a rapper, record producer, and entrepreneur. He will be co-hosted by Nick Montfort of Comparative Media Studies/Writing and Mary Fuller of Literature. Jaco’s interests lie in the nexus of rap, computing, and activism. As a former visiting artist in MIT’s Center for Art, Science and Technology (CAST), he will leverage existing collaborations and participate in digital media and art research projects that use computing to explore novel questions related to hip-hop and rap. In addition to his engagement in cross-departmental projects, Jaco will teach a spring course on rap in the media and social contexts.

      Moribah Jah is an associate professor in the Aerospace Engineering and Engineering Mechanics Department at the University of Texas at Austin. He is hosted by Danielle Wood in Media Arts and Sciences and the Department of Aeronautics and Astronautics, and Richard Linares in the Department of Aeronautics and Astronautics. Jah’s research interests include space sustainability and space traffic management; as a visiting scholar, he will develop and strengthen a joint MIT/UT-Austin research program to increase resources and visibility of space sustainability. Jah will also help host the AeroAstro Rising Stars symposium, which highlights graduate students, postdocs, and early-career faculty from backgrounds underrepresented in aerospace engineering. 

      Louis Massiah SM ’82 is a documentary filmmaker and the founder and director of community media of Scribe Video Center, a nonprofit organization that uses media as a tool for social change. His work focuses on empowering Black, Indigenous, and People of Color (BIPOC) filmmakers to tell the stories of/by BIPOC communities. Massiah is hosted by Vivek Bald in Creative Media Studies/Writing. Massiah’s first project will be the launch of a National Community Media Journalism Consortium, a platform to share local news on a broader scale across communities.

      Brian Nord, a scientist at Fermi National Accelerator Laboratory, will join the Laboratory for Nuclear Science, hosted by Jesse Thaler in the Department of Physics. Nord’s research interests include the connection between ethics, justice, and scientific discovery. His efforts will be aimed at introducing new insights into how we model physical systems, design scientific experiments, and approach the ethics of artificial intelligence. As a lead organizer of the Strike for Black Lives in 2020, Nord will engage with justice-oriented members of the MIT physics community to strategize actions for advocacy and activism.

      Brandon Ogbunu, an assistant professor in the Department of Ecology and Evolutionary Biology at Yale University, will be hosted by Matthew Shoulders in the Department of Chemistry. Ogbunu’s research focus is on implementing chemistry and materials science perspectives into his work on virus evolution. In addition to serving as a guest lecturer in graduate courses, he will be collaborating with the Office of Engineering Outreach Programs on their K-12 outreach and recruitment efforts.

      For more information about these scholars and the program, visit mlkscholars.mit.edu. More