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    New leadership at MIT’s Center for Biomedical Innovation

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

    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

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    Mining social media data for social good

    For Erin Walk, who has loved school since she was a little girl, pursuing a graduate degree always seemed like a given. As a mechanical engineering major at Harvard University with a minor in government, she figured that going to graduate school in engineering would be the next logical step. However, during her senior year, a class on the “Technology of War” changed her trajectory, sparking her interest in technology and policy.

    “[Warfare] seems like a very dark reason for this interest to blossom … but I was so interested in how these technological developments including cyberwar had such a large impact on the entire course of world history,” Walk says. The class took a starkly different perspective from her engineering classes, which often focused on how a revolutionary technology was built. Instead, Walk was challenged to think about “the implications of what this [technology] could do.” 

    Now, Walk is studying the intersection between data science, policy, and technology as a graduate student in the Social and Engineering Systems program (SES), part of the Institute for Data, Systems, and Society (IDSS). Her research has demonstrated the value and bias inherent in social media data, with a focus on how to mine social media data to better understand the conflict in Syria. 

    Using data for social good

    With a newfound interest in policy developing just as college was drawing to a close, Walk says, “I realized I did not know what I wanted to do research on for five whole years, and the idea of getting a PhD started to feel very daunting.” Instead, she decided to work for a web security company in Washington, as a member of the policy team. “Being in school can be this fast process where you feel like you are being pushed through a tube and all of a sudden you come out the other end. Work gave me a lot more mental time to think about what I enjoyed and what was important to me,” she says.

    Walk served as a liaison between thinktanks and nonprofits in Washington that worked to provide services and encourage policies that enable equitable technology distribution. The role helped her identify what held her interest: corporate social responsibility projects that addressed access to technology, in this case, by donating free web security services to nonprofit organizations and to election websites. She became curious about how access to data and to the Internet can be beneficial for education, and how such access can be leveraged to establish connections to populations that are otherwise hard-to-reach, such as refugees, marginalized groups, or activist communities that rely on anonymity for safety.

    Walk knew she wanted to pursue this kind of tech activism work, but she also recognized that staying with a company driven by profits would not be the best avenue to fulfill her personal career aspirations. Graduate school seemed like the best option to both learn the data science skills she needed, and pursue full-time research focusing on technology and policy.

    Finding new ways to tap social media data

    With these goals in mind, Walk joined the SES graduate program in IDSS. “This program for me had the most balance,” she says. “I have a lot of leeway to explore whatever kind of research I want, provided it has an impact component and a data component.”

    During her first year, she intended to explore a variety of research advisors to find the right fit. Instead, during her first few months on MIT’s campus, she sat down for an introductory meeting with her now-research advisor, Fotini Christia, the Ford International Professor in the Social Sciences, and walked out with a project. Her new task: analyzing “how different social media sources are used differently by groups within the conflict, and how those different narratives present themselves online. So much social science research tends to use just Twitter, or just Facebook, to draw conclusions. It is important to understand how your data set might be skewed,” she says.

    Walk’s current research focuses on another novel way to tap social media. Scholars traditionally use geographic data to understand population movements, but her research has demonstrated that social media can also be a ripe data source. She is analyzing how social media discussions differ in places with and without refugees, with a particular focus on places where refugees have returned to their homelands, including Syria.

    “Now that the [Syrian] civil war has been going on for so long, there is a lot of discussion on how to bring refugees back in [to their homelands],” Walk says. Her research adds to this discussion by using social media sources to understand and predict the factors that encourage refugees to return, such as economic opportunities and decreases in local violence. Her goal is to harness some of the social media data to provide policymakers and nonprofits with information on how to address repatriation and related issues.

    Walk attributes much of her growth as a graduate student to the influence of collaborators, especially Professor Kiran Garimella at Rutgers’ Department of Library and Information Science. “So much of being a graduate student is feeling like you have a stupid question and figuring out who you can be vulnerable with in asking that stupid question,” she says. “I am very lucky to have a lot of those people in my life.”

    Encouraging the next generation

    Now, as a third-year student, Walk is the one whom others go to with their “stupid questions.” This desire to mentor and share her knowledge extends beyond the laboratory. “Something I discovered is that I really like talking to and advising people who are in a similar position to where I was. It is fulfilling to work with smart people close to my age who are just trying to figure out the answers to these meaty life issues that I have also struggled with,” she says.

    This realization led Walk to a position as a resident advisor at Harvard University’s Mather House, an undergraduate dormitory and community center. Walk became a faculty dean aide during her first year at MIT, and since then has served as a full-time Mather House resident tutor. “Every year I advise a new class of students, and I just become invested in their process. I get to talk to people about their lives, about their classes, about what is making them excited and about what is making them sad,” she says.

    After she graduates, Walk plans to explore issues that have a positive, tangible impact on policy outcomes and people, perhaps in an academic lab or in a nonprofit organization. Two such issues that particularly intrigue her are internet access and privacy for underserved populations. Regardless of the issues, she will continue to draw from both political science and data science. “One of my favorite things about being a part of interdisciplinary research is that [experts in] political science and computer science approach these issues so differently, and it is very grounding to have both of those perspectives. Political science thinks so carefully about measurement, population selection, and research design … [while] computer science has so many interesting methods that should be used in other disciplines,” she says.

    No matter what the future holds, Walk already has a sense of contentment. She admits that “my path was much less linear than I expected. I don’t think I even realized that a field like this existed.” Nevertheless, she says with a laugh, “I think that little-girl me would be very proud of present-day me.” More

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    On the road to cleaner, greener, and faster driving

    No one likes sitting at a red light. But signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change.

    What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just a lucky break for a human driver, it could be achieved more consistently by an autonomous vehicle that uses artificial intelligence to control its speed.

    In a new study, MIT researchers demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly.

    Using simulations, they found that their approach reduces fuel consumption and emissions while improving average vehicle speed. The technique gets the best results if all cars on the road are autonomous, but even if only 25 percent use their control algorithm, it still leads to substantial fuel and emissions benefits.

    “This is a really interesting place to intervene. No one’s life is better because they were stuck at an intersection. With a lot of other climate change interventions, there is a quality-of-life difference that is expected, so there is a barrier to entry there. Here, the barrier is much lower,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in the Department of Civil and Environmental Engineering and a member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS).

    The lead author of the study is Vindula Jayawardana, a graduate student in LIDS and the Department of Electrical Engineering and Computer Science. The research will be presented at the European Control Conference.

    Intersection intricacies

    While humans may drive past a green light without giving it much thought, intersections can present billions of different scenarios depending on the number of lanes, how the signals operate, the number of vehicles and their speeds, the presence of pedestrians and cyclists, etc.

    Typical approaches for tackling intersection control problems use mathematical models to solve one simple, ideal intersection. That looks good on paper, but likely won’t hold up in the real world, where traffic patterns are often about as messy as they come.

    Wu and Jayawardana shifted gears and approached the problem using a model-free technique known as deep reinforcement learning. Reinforcement learning is a trial-and-error method where the control algorithm learns to make a sequence of decisions. It is rewarded when it finds a good sequence. With deep reinforcement learning, the algorithm leverages assumptions learned by a neural network to find shortcuts to good sequences, even if there are billions of possibilities.

    This is useful for solving a long-horizon problem like this; the control algorithm must issue upwards of 500 acceleration instructions to a vehicle over an extended time period, Wu explains.

    “And we have to get the sequence right before we know that we have done a good job of mitigating emissions and getting to the intersection at a good speed,” she adds.

    But there’s an additional wrinkle. The researchers want the system to learn a strategy that reduces fuel consumption and limits the impact on travel time. These goals can be conflicting.

    “To reduce travel time, we want the car to go fast, but to reduce emissions, we want the car to slow down or not move at all. Those competing rewards can be very confusing to the learning agent,” Wu says.

    While it is challenging to solve this problem in its full generality, the researchers employed a workaround using a technique known as reward shaping. With reward shaping, they give the system some domain knowledge it is unable to learn on its own. In this case, they penalized the system whenever the vehicle came to a complete stop, so it would learn to avoid that action.

    Traffic tests

    Once they developed an effective control algorithm, they evaluated it using a traffic simulation platform with a single intersection. The control algorithm is applied to a fleet of connected autonomous vehicles, which can communicate with upcoming traffic lights to receive signal phase and timing information and observe their immediate surroundings. The control algorithm tells each vehicle how to accelerate and decelerate.

    Their system didn’t create any stop-and-go traffic as vehicles approached the intersection. (Stop-and-go traffic occurs when cars are forced to come to a complete stop due to stopped traffic ahead). In simulations, more cars made it through in a single green phase, which outperformed a model that simulates human drivers. When compared to other optimization methods also designed to avoid stop-and-go traffic, their technique resulted in larger fuel consumption and emissions reductions. If every vehicle on the road is autonomous, their control system can reduce fuel consumption by 18 percent and carbon dioxide emissions by 25 percent, while boosting travel speeds by 20 percent.

    “A single intervention having 20 to 25 percent reduction in fuel or emissions is really incredible. But what I find interesting, and was really hoping to see, is this non-linear scaling. If we only control 25 percent of vehicles, that gives us 50 percent of the benefits in terms of fuel and emissions reduction. That means we don’t have to wait until we get to 100 percent autonomous vehicles to get benefits from this approach,” she says.

    Down the road, the researchers want to study interaction effects between multiple intersections. They also plan to explore how different intersection set-ups (number of lanes, signals, timings, etc.) can influence travel time, emissions, and fuel consumption. In addition, they intend to study how their control system could impact safety when autonomous vehicles and human drivers share the road. For instance, even though autonomous vehicles may drive differently than human drivers, slower roadways and roadways with more consistent speeds could improve safety, Wu says.

    While this work is still in its early stages, Wu sees this approach as one that could be more feasibly implemented in the near-term.

    “The aim in this work is to move the needle in sustainable mobility. We want to dream, as well, but these systems are big monsters of inertia. Identifying points of intervention that are small changes to the system but have significant impact is something that gets me up in the morning,” she says.  

    This work was supported, in part, by the MIT-IBM Watson AI Lab. More

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    Engineers use artificial intelligence to capture the complexity of breaking waves

    Waves break once they swell to a critical height, before cresting and crashing into a spray of droplets and bubbles. These waves can be as large as a surfer’s point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too complex to predict.

    Now, MIT engineers have found a new way to model how waves break. The team used machine learning along with data from wave-tank experiments to tweak equations that have traditionally been used to predict wave behavior. Engineers typically rely on such equations to help them design resilient offshore platforms and structures. But until now, the equations have not been able to capture the complexity of breaking waves.

    The updated model made more accurate predictions of how and when waves break, the researchers found. For instance, the model estimated a wave’s steepness just before breaking, and its energy and frequency after breaking, more accurately than the conventional wave equations.

    Their results, published today in the journal Nature Communications, will help scientists understand how a breaking wave affects the water around it. Knowing precisely how these waves interact can help hone the design of offshore structures. It can also improve predictions for how the ocean interacts with the atmosphere. Having better estimates of how waves break can help scientists predict, for instance, how much carbon dioxide and other atmospheric gases the ocean can absorb.

    “Wave breaking is what puts air into the ocean,” says study author Themis Sapsis, an associate professor of mechanical and ocean engineering and an affiliate of the Institute for Data, Systems, and Society at MIT. “It may sound like a detail, but if you multiply its effect over the area of the entire ocean, wave breaking starts becoming fundamentally important to climate prediction.”

    The study’s co-authors include lead author and MIT postdoc Debbie Eeltink, Hubert Branger and Christopher Luneau of Aix-Marseille University, Amin Chabchoub of Kyoto University, Jerome Kasparian of the University of Geneva, and T.S. van den Bremer of Delft University of Technology.

    Learning tank

    To predict the dynamics of a breaking wave, scientists typically take one of two approaches: They either attempt to precisely simulate the wave at the scale of individual molecules of water and air, or they run experiments to try and characterize waves with actual measurements. The first approach is computationally expensive and difficult to simulate even over a small area; the second requires a huge amount of time to run enough experiments to yield statistically significant results.

    The MIT team instead borrowed pieces from both approaches to develop a more efficient and accurate model using machine learning. The researchers started with a set of equations that is considered the standard description of wave behavior. They aimed to improve the model by “training” the model on data of breaking waves from actual experiments.

    “We had a simple model that doesn’t capture wave breaking, and then we had the truth, meaning experiments that involve wave breaking,” Eeltink explains. “Then we wanted to use machine learning to learn the difference between the two.”

    The researchers obtained wave breaking data by running experiments in a 40-meter-long tank. The tank was fitted at one end with a paddle which the team used to initiate each wave. The team set the paddle to produce a breaking wave in the middle of the tank. Gauges along the length of the tank measured the water’s height as waves propagated down the tank.

    “It takes a lot of time to run these experiments,” Eeltink says. “Between each experiment you have to wait for the water to completely calm down before you launch the next experiment, otherwise they influence each other.”

    Safe harbor

    In all, the team ran about 250 experiments, the data from which they used to train a type of machine-learning algorithm known as a neural network. Specifically, the algorithm is trained to compare the real waves in experiments with the predicted waves in the simple model, and based on any differences between the two, the algorithm tunes the model to fit reality.

    After training the algorithm on their experimental data, the team introduced the model to entirely new data — in this case, measurements from two independent experiments, each run at separate wave tanks with different dimensions. In these tests, they found the updated model made more accurate predictions than the simple, untrained model, for instance making better estimates of a breaking wave’s steepness.

    The new model also captured an essential property of breaking waves known as the “downshift,” in which the frequency of a wave is shifted to a lower value. The speed of a wave depends on its frequency. For ocean waves, lower frequencies move faster than higher frequencies. Therefore, after the downshift, the wave will move faster. The new model predicts the change in frequency, before and after each breaking wave, which could be especially relevant in preparing for coastal storms.

    “When you want to forecast when high waves of a swell would reach a harbor, and you want to leave the harbor before those waves arrive, then if you get the wave frequency wrong, then the speed at which the waves are approaching is wrong,” Eeltink says.

    The team’s updated wave model is in the form of an open-source code that others could potentially use, for instance in climate simulations of the ocean’s potential to absorb carbon dioxide and other atmospheric gases. The code can also be worked into simulated tests of offshore platforms and coastal structures.

    “The number one purpose of this model is to predict what a wave will do,” Sapsis says. “If you don’t model wave breaking right, it would have tremendous implications for how structures behave. With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors.”

    This research is supported, in part, by the Swiss National Science Foundation, and by the U.S. Office of Naval Research. More

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    Seven from MIT elected to American Academy of Arts and Sciences for 2022

    Seven MIT faculty members are among more than 250 leaders from academia, the arts, industry, public policy, and research elected to the American Academy of Arts and Sciences, the academy announced Thursday.

    One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

    Those elected from MIT this year are:

    Alberto Abadie, professor of economics and associate director of the Institute for Data, Systems, and Society
    Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health
    Roman Bezrukavnikov, professor of mathematics
    Michale S. Fee, the Glen V. and Phyllis F. Dorflinger Professor and head of the Department of Brain and Cognitive Sciences
    Dina Katabi, the Thuan and Nicole Pham Professor
    Ronald T. Raines, the Roger and Georges Firmenich Professor of Natural Products Chemistry
    Rebecca R. Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences

    “We are celebrating a depth of achievements in a breadth of areas,” says David Oxtoby, president of the American Academy. “These individuals excel in ways that excite us and inspire us at a time when recognizing excellence, commending expertise, and working toward the common good is absolutely essential to realizing a better future.”

    Since its founding in 1780, the academy has elected leading thinkers from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 250 Nobel and Pulitzer Prize winners. More

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    Looking forward to forecast the risks of a changing climate

    On April 11, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the third in a five-part series highlighting the most promising concepts to emerge from the competition, and the interdisciplinary research teams behind them.

    Extreme weather events that were once considered rare have become noticeably less so, from intensifying hurricane activity in the North Atlantic to wildfires generating massive clouds of ozone-damaging smoke. But current climate models are unprepared when it comes to estimating the risk that these increasingly extreme events pose — and without adequate modeling, governments are left unable to take necessary precautions to protect their communities.

    MIT Department of Earth, Atmospheric and Planetary Science (EAPS) Professor Paul O’Gorman researches this trend by studying how climate affects the atmosphere and incorporating what he learns into climate models to improve their accuracy. One particular focus for O’Gorman has been changes in extreme precipitation and midlatitude storms that hit areas like New England.

    “These extreme events are having a lot of impact, but they’re also difficult to model or study,” he says. Seeing the pressing need for better climate models that can be used to develop preparedness plans and climate change mitigation strategies, O’Gorman and collaborators Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in EAPS, and Miho Mazereeuw, associate professor in MIT’s Department of Architecture, are leading an interdisciplinary group of scientists, engineers, and designers to tackle this problem with their MIT Climate Grand Challenges flagship project, “Preparing for a new world of weather and climate extremes.”

    “We know already from observations and from climate model predictions that weather and climate extremes are changing and will change more,” O’Gorman says. “The grand challenge is preparing for those changing extremes.”

    Their proposal is one of five flagship projects recently announced by the MIT Climate Grand Challenges initiative — an Institute-wide effort catalyzing novel research and engineering innovations to address the climate crisis. Selected from a field of almost 100 submissions, the team will receive additional funding and exposure to help accelerate and scale their project goals. Other MIT collaborators on the proposal include researchers from the School of Engineering, the School of Architecture and Planning, the Office of Sustainability, the Center for Global Change Science, and the Institute for Data, Systems and Society.

    Weather risk modeling

    Fifteen years ago, Kerry Emanuel developed a simple hurricane model. It was based on physics equations, rather than statistics, and could run in real time, making it useful for modeling risk assessment. Emanuel wondered if similar models could be used for long-term risk assessment of other things, such as changes in extreme weather because of climate change.

    “I discovered, somewhat to my surprise and dismay, that almost all extant estimates of long-term weather risks in the United States are based not on physical models, but on historical statistics of the hazards,” says Emanuel. “The problem with relying on historical records is that they’re too short; while they can help estimate common events, they don’t contain enough information to make predictions for more rare events.”

    Another limitation of weather risk models which rely heavily on statistics: They have a built-in assumption that the climate is static.

    “Historical records rely on the climate at the time they were recorded; they can’t say anything about how hurricanes grow in a warmer climate,” says Emanuel. The models rely on fixed relationships between events; they assume that hurricane activity will stay the same, even while science is showing that warmer temperatures will most likely push typical hurricane activity beyond the tropics and into a much wider band of latitudes.

    As a flagship project, the goal is to eliminate this reliance on the historical record by emphasizing physical principles (e.g., the laws of thermodynamics and fluid mechanics) in next-generation models. The downside to this is that there are many variables that have to be included. Not only are there planetary-scale systems to consider, such as the global circulation of the atmosphere, but there are also small-scale, extremely localized events, like thunderstorms, that influence predictive outcomes.

    Trying to compute all of these at once is costly and time-consuming — and the results often can’t tell you the risk in a specific location. But there is a way to correct for this: “What’s done is to use a global model, and then use a method called downscaling, which tries to infer what would happen on very small scales that aren’t properly resolved by the global model,” explains O’Gorman. The team hopes to improve downscaling techniques so that they can be used to calculate the risk of very rare but impactful weather events.

    Global climate models, or general circulation models (GCMs), Emanuel explains, are constructed a bit like a jungle gym. Like the playground bars, the Earth is sectioned in an interconnected three-dimensional framework — only it’s divided 100 to 200 square kilometers at a time. Each node comprises a set of computations for characteristics like wind, rainfall, atmospheric pressure, and temperature within its bounds; the outputs of each node are connected to its neighbor. This framework is useful for creating a big picture idea of Earth’s climate system, but if you tried to zoom in on a specific location — like, say, to see what’s happening in Miami or Mumbai — the connecting nodes are too far apart to make predictions on anything specific to those areas.

    Scientists work around this problem by using downscaling. They use the same blueprint of the jungle gym, but within the nodes they weave a mesh of smaller features, incorporating equations for things like topography and vegetation or regional meteorological models to fill in the blanks. By creating a finer mesh over smaller areas they can predict local effects without needing to run the entire global model.

    Of course, even this finer-resolution solution has its trade-offs. While we might be able to gain a clearer picture of what’s happening in a specific region by nesting models within models, it can still make for a computing challenge to crunch all that data at once, with the trade-off being expense and time, or predictions that are limited to shorter windows of duration — where GCMs can be run considering decades or centuries, a particularly complex local model may be restricted to predictions on timescales of just a few years at a time.

    “I’m afraid that most of the downscaling at present is brute force, but I think there’s room to do it in better ways,” says Emanuel, who sees the problem of finding new and novel methods of achieving this goal as an intellectual challenge. “I hope that through the Grand Challenges project we might be able to get students, postdocs, and others interested in doing this in a very creative way.”

    Adapting to weather extremes for cities and renewable energy

    Improving climate modeling is more than a scientific exercise in creativity, however. There’s a very real application for models that can accurately forecast risk in localized regions.

    Another problem is that progress in climate modeling has not kept up with the need for climate mitigation plans, especially in some of the most vulnerable communities around the globe.

    “It is critical for stakeholders to have access to this data for their own decision-making process. Every community is composed of a diverse population with diverse needs, and each locality is affected by extreme weather events in unique ways,” says Mazereeuw, the director of the MIT Urban Risk Lab. 

    A key piece of the team’s project is building on partnerships the Urban Risk Lab has developed with several cities to test their models once they have a usable product up and running. The cities were selected based on their vulnerability to increasing extreme weather events, such as tropical cyclones in Broward County, Florida, and Toa Baja, Puerto Rico, and extratropical storms in Boston, Massachusetts, and Cape Town, South Africa.

    In their proposal, the team outlines a variety of deliverables that the cities can ultimately use in their climate change preparations, with ideas such as online interactive platforms and workshops with stakeholders — such as local governments, developers, nonprofits, and residents — to learn directly what specific tools they need for their local communities. By doing so, they can craft plans addressing different scenarios in their region, involving events such as sea-level rise or heat waves, while also providing information and means of developing adaptation strategies for infrastructure under these conditions that will be the most effective and efficient for them.

    “We are acutely aware of the inequity of resources both in mitigating impacts and recovering from disasters. Working with diverse communities through workshops allows us to engage a lot of people, listen, discuss, and collaboratively design solutions,” says Mazereeuw.

    By the end of five years, the team is hoping that they’ll have better risk assessment and preparedness tool kits, not just for the cities that they’re partnering with, but for others as well.

    “MIT is well-positioned to make progress in this area,” says O’Gorman, “and I think it’s an important problem where we can make a difference.” More

  • in

    Computing our climate future

    On Monday, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the first in a five-part series highlighting the most promising concepts to emerge from the competition, and the interdisciplinary research teams behind them.

    With improvements to computer processing power and an increased understanding of the physical equations governing the Earth’s climate, scientists are continually working to refine climate models and improve their predictive power. But the tools they’re refining were originally conceived decades ago with only scientists in mind. When it comes to developing tangible climate action plans, these models remain inscrutable to the policymakers, public safety officials, civil engineers, and community organizers who need their predictive insight most.

    “What you end up having is a gap between what’s typically used in practice, and the real cutting-edge science,” says Noelle Selin, a professor in the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences (EAPS), and co-lead with Professor Raffaele Ferrari on the MIT Climate Grand Challenges flagship project “Bringing Computation to the Climate Crisis.” “How can we use new computational techniques, new understandings, new ways of thinking about modeling, to really bridge that gap between state-of-the-art scientific advances and modeling, and people who are actually needing to use these models?”

    Using this as a driving question, the team won’t just be trying to refine current climate models, they’re building a new one from the ground up.

    This kind of game-changing advancement is exactly what the MIT Climate Grand Challenges is looking for, which is why the proposal has been named one of the five flagship projects in the ambitious Institute-wide program aimed at tackling the climate crisis. The proposal, which was selected from 100 submissions and was among 27 finalists, will receive additional funding and support to further their goal of reimagining the climate modeling system. It also brings together contributors from across the Institute, including the MIT Schwarzman College of Computing, the School of Engineering, and the Sloan School of Management.

    When it comes to pursuing high-impact climate solutions that communities around the world can use, “it’s great to do it at MIT,” says Ferrari, EAPS Cecil and Ida Green Professor of Oceanography. “You’re not going to find many places in the world where you have the cutting-edge climate science, the cutting-edge computer science, and the cutting-edge policy science experts that we need to work together.”

    The climate model of the future

    The proposal builds on work that Ferrari began three years ago as part of a joint project with Caltech, the Naval Postgraduate School, and NASA’s Jet Propulsion Lab. Called the Climate Modeling Alliance (CliMA), the consortium of scientists, engineers, and applied mathematicians is constructing a climate model capable of more accurately projecting future changes in critical variables, such as clouds in the atmosphere and turbulence in the ocean, with uncertainties at least half the size of those in existing models.

    To do this, however, requires a new approach. For one thing, current models are too coarse in resolution — at the 100-to-200-kilometer scale — to resolve small-scale processes like cloud cover, rainfall, and sea ice extent. But also, explains Ferrari, part of this limitation in resolution is due to the fundamental architecture of the models themselves. The languages most global climate models are coded in were first created back in the 1960s and ’70s, largely by scientists for scientists. Since then, advances in computing driven by the corporate world and computer gaming have given rise to dynamic new computer languages, powerful graphics processing units, and machine learning.

    For climate models to take full advantage of these advancements, there’s only one option: starting over with a modern, more flexible language. Written in Julia, a part of Julialab’s Scientific Machine Learning technology, and spearheaded by Alan Edelman, a professor of applied mathematics in MIT’s Department of Mathematics, CliMA will be able to harness far more data than the current models can handle.

    “It’s been real fun finally working with people in computer science here at MIT,” Ferrari says. “Before it was impossible, because traditional climate models are in a language their students can’t even read.”

    The result is what’s being called the “Earth digital twin,” a climate model that can simulate global conditions on a large scale. This on its own is an impressive feat, but the team wants to take this a step further with their proposal.

    “We want to take this large-scale model and create what we call an ‘emulator’ that is only predicting a set of variables of interest, but it’s been trained on the large-scale model,” Ferrari explains. Emulators are not new technology, but what is new is that these emulators, being referred to as the “Earth digital cousins,” will take advantage of machine learning.

    “Now we know how to train a model if we have enough data to train them on,” says Ferrari. Machine learning for projects like this has only become possible in recent years as more observational data become available, along with improved computer processing power. The goal is to create smaller, more localized models by training them using the Earth digital twin. Doing so will save time and money, which is key if the digital cousins are going to be usable for stakeholders, like local governments and private-sector developers.

    Adaptable predictions for average stakeholders

    When it comes to setting climate-informed policy, stakeholders need to understand the probability of an outcome within their own regions — in the same way that you would prepare for a hike differently if there’s a 10 percent chance of rain versus a 90 percent chance. The smaller Earth digital cousin models will be able to do things the larger model can’t do, like simulate local regions in real time and provide a wider range of probabilistic scenarios.

    “Right now, if you wanted to use output from a global climate model, you usually would have to use output that’s designed for general use,” says Selin, who is also the director of the MIT Technology and Policy Program. With the project, the team can take end-user needs into account from the very beginning while also incorporating their feedback and suggestions into the models, helping to “democratize the idea of running these climate models,” as she puts it. Doing so means building an interactive interface that eventually will give users the ability to change input values and run the new simulations in real time. The team hopes that, eventually, the Earth digital cousins could run on something as ubiquitous as a smartphone, although developments like that are currently beyond the scope of the project.

    The next thing the team will work on is building connections with stakeholders. Through participation of other MIT groups, such as the Joint Program on the Science and Policy of Global Change and the Climate and Sustainability Consortium, they hope to work closely with policymakers, public safety officials, and urban planners to give them predictive tools tailored to their needs that can provide actionable outputs important for planning. Faced with rising sea levels, for example, coastal cities could better visualize the threat and make informed decisions about infrastructure development and disaster preparedness; communities in drought-prone regions could develop long-term civil planning with an emphasis on water conservation and wildfire resistance.

    “We want to make the modeling and analysis process faster so people can get more direct and useful feedback for near-term decisions,” she says.

    The final piece of the challenge is to incentivize students now so that they can join the project and make a difference. Ferrari has already had luck garnering student interest after co-teaching a class with Edelman and seeing the enthusiasm students have about computer science and climate solutions.

    “We’re intending in this project to build a climate model of the future,” says Selin. “So it seems really appropriate that we would also train the builders of that climate model.” More