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      A far-sighted approach to machine learning

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      Picture two teams squaring off on a football field. The players can cooperate to achieve an objective, and compete against other players with conflicting interests. That’s how the game works.

      Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents to anticipate future behaviors of other agents when they are all learning simultaneously.

      Because of the complexity of this problem, current approaches tend to be myopic; the agents can only guess the next few moves of their teammates or competitors, which leads to poor performance in the long run. 

      Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a new approach that gives AI agents a farsighted perspective. Their machine-learning framework enables cooperative or competitive AI agents to consider what other agents will do as time approaches infinity, not just over a few next steps. The agents then adapt their behaviors accordingly to influence other agents’ future behaviors and arrive at an optimal, long-term solution.

      This framework could be used by a group of autonomous drones working together to find a lost hiker in a thick forest, or by self-driving cars that strive to keep passengers safe by anticipating future moves of other vehicles driving on a busy highway.

      “When AI agents are cooperating or competing, what matters most is when their behaviors converge at some point in the future. There are a lot of transient behaviors along the way that don’t matter very much in the long run. Reaching this converged behavior is what we really care about, and we now have a mathematical way to enable that,” says Dong-Ki Kim, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS) and lead author of a paper describing this framework.

      The senior author is Jonathan P. How, the Richard C. Maclaurin Professor of Aeronautics and Astronautics and a member of the MIT-IBM Watson AI Lab. Co-authors include others at the MIT-IBM Watson AI Lab, IBM Research, Mila-Quebec Artificial Intelligence Institute, and Oxford University. The research will be presented at the Conference on Neural Information Processing Systems.

      Play video

      In this demo video, the red robot, which has been trained using the researchers’ machine-learning system, is able to defeat the green robot by learning more effective behaviors that take advantage of the constantly changing strategy of its opponent.

      More agents, more problems

      The researchers focused on a problem known as multiagent reinforcement learning. Reinforcement learning is a form of machine learning in which an AI agent learns by trial and error. Researchers give the agent a reward for “good” behaviors that help it achieve a goal. The agent adapts its behavior to maximize that reward until it eventually becomes an expert at a task.

      But when many cooperative or competing agents are simultaneously learning, things become increasingly complex. As agents consider more future steps of their fellow agents, and how their own behavior influences others, the problem soon requires far too much computational power to solve efficiently. This is why other approaches only focus on the short term.

      “The AIs really want to think about the end of the game, but they don’t know when the game will end. They need to think about how to keep adapting their behavior into infinity so they can win at some far time in the future. Our paper essentially proposes a new objective that enables an AI to think about infinity,” says Kim.

      But since it is impossible to plug infinity into an algorithm, the researchers designed their system so agents focus on a future point where their behavior will converge with that of other agents, known as equilibrium. An equilibrium point determines the long-term performance of agents, and multiple equilibria can exist in a multiagent scenario. Therefore, an effective agent actively influences the future behaviors of other agents in such a way that they reach a desirable equilibrium from the agent’s perspective. If all agents influence each other, they converge to a general concept that the researchers call an “active equilibrium.”

      The machine-learning framework they developed, known as FURTHER (which stands for FUlly Reinforcing acTive influence witH averagE Reward), enables agents to learn how to adapt their behaviors as they interact with other agents to achieve this active equilibrium.

      FURTHER does this using two machine-learning modules. The first, an inference module, enables an agent to guess the future behaviors of other agents and the learning algorithms they use, based solely on their prior actions.

      This information is fed into the reinforcement learning module, which the agent uses to adapt its behavior and influence other agents in a way that maximizes its reward.

      “The challenge was thinking about infinity. We had to use a lot of different mathematical tools to enable that, and make some assumptions to get it to work in practice,” Kim says.

      Winning in the long run

      They tested their approach against other multiagent reinforcement learning frameworks in several different scenarios, including a pair of robots fighting sumo-style and a battle pitting two 25-agent teams against one another. In both instances, the AI agents using FURTHER won the games more often.

      Since their approach is decentralized, which means the agents learn to win the games independently, it is also more scalable than other methods that require a central computer to control the agents, Kim explains.

      The researchers used games to test their approach, but FURTHER could be used to tackle any kind of multiagent problem. For instance, it could be applied by economists seeking to develop sound policy in situations where many interacting entitles have behaviors and interests that change over time.

      Economics is one application Kim is particularly excited about studying. He also wants to dig deeper into the concept of an active equilibrium and continue enhancing the FURTHER framework.

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

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      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

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      in Data Management & Statistics

      Researchers release open-source photorealistic simulator for autonomous driving

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      Hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs), since they’ve proven fruitful test beds for safely trying out dangerous driving scenarios. Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, since testing and gathering nuanced I-almost-crashed data usually isn’t the most easy or desirable to recreate. 

      To that end, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created “VISTA 2.0,” a data-driven simulation engine where vehicles can learn to drive in the real world and recover from near-crash scenarios. What’s more, all of the code is being open-sourced to the public. 

      “Today, only companies have software like the type of simulation environments and capabilities of VISTA 2.0, and this software is proprietary. With this release, the research community will have access to a powerful new tool for accelerating the research and development of adaptive robust control for autonomous driving,” says MIT Professor and CSAIL Director Daniela Rus, senior author on a paper about the research. 

      Play video

      VISTA is a data-driven, photorealistic simulator for autonomous driving. It can simulate not just live video but LiDAR data and event cameras, and also incorporate other simulated vehicles to model complex driving situations. VISTA is open source and the code can be found below.

      VISTA 2.0 builds off of the team’s previous model, VISTA, and it’s fundamentally different from existing AV simulators since it’s data-driven — meaning it was built and photorealistically rendered from real-world data — thereby enabling direct transfer to reality. While the initial iteration supported only single car lane-following with one camera sensor, achieving high-fidelity data-driven simulation required rethinking the foundations of how different sensors and behavioral interactions can be synthesized. 

      Enter VISTA 2.0: a data-driven system that can simulate complex sensor types and massively interactive scenarios and intersections at scale. With much less data than previous models, the team was able to train autonomous vehicles that could be substantially more robust than those trained on large amounts of real-world data. 

      “This is a massive jump in capabilities of data-driven simulation for autonomous vehicles, as well as the increase of scale and ability to handle greater driving complexity,” says Alexander Amini, CSAIL PhD student and co-lead author on two new papers, together with fellow PhD student Tsun-Hsuan Wang. “VISTA 2.0 demonstrates the ability to simulate sensor data far beyond 2D RGB cameras, but also extremely high dimensional 3D lidars with millions of points, irregularly timed event-based cameras, and even interactive and dynamic scenarios with other vehicles as well.” 

      The team was able to scale the complexity of the interactive driving tasks for things like overtaking, following, and negotiating, including multiagent scenarios in highly photorealistic environments. 

      Training AI models for autonomous vehicles involves hard-to-secure fodder of different varieties of edge cases and strange, dangerous scenarios, because most of our data (thankfully) is just run-of-the-mill, day-to-day driving. Logically, we can’t just crash into other cars just to teach a neural network how to not crash into other cars.

      Recently, there’s been a shift away from more classic, human-designed simulation environments to those built up from real-world data. The latter have immense photorealism, but the former can easily model virtual cameras and lidars. With this paradigm shift, a key question has emerged: Can the richness and complexity of all of the sensors that autonomous vehicles need, such as lidar and event-based cameras that are more sparse, accurately be synthesized? 

      Lidar sensor data is much harder to interpret in a data-driven world — you’re effectively trying to generate brand-new 3D point clouds with millions of points, only from sparse views of the world. To synthesize 3D lidar point clouds, the team used the data that the car collected, projected it into a 3D space coming from the lidar data, and then let a new virtual vehicle drive around locally from where that original vehicle was. Finally, they projected all of that sensory information back into the frame of view of this new virtual vehicle, with the help of neural networks. 

      Together with the simulation of event-based cameras, which operate at speeds greater than thousands of events per second, the simulator was capable of not only simulating this multimodal information, but also doing so all in real time — making it possible to train neural nets offline, but also test online on the car in augmented reality setups for safe evaluations. “The question of if multisensor simulation at this scale of complexity and photorealism was possible in the realm of data-driven simulation was very much an open question,” says Amini. 

      With that, the driving school becomes a party. In the simulation, you can move around, have different types of controllers, simulate different types of events, create interactive scenarios, and just drop in brand new vehicles that weren’t even in the original data. They tested for lane following, lane turning, car following, and more dicey scenarios like static and dynamic overtaking (seeing obstacles and moving around so you don’t collide). With the multi-agency, both real and simulated agents interact, and new agents can be dropped into the scene and controlled any which way. 

      Taking their full-scale car out into the “wild” — a.k.a. Devens, Massachusetts — the team saw  immediate transferability of results, with both failures and successes. They were also able to demonstrate the bodacious, magic word of self-driving car models: “robust.” They showed that AVs, trained entirely in VISTA 2.0, were so robust in the real world that they could handle that elusive tail of challenging failures. 

      Now, one guardrail humans rely on that can’t yet be simulated is human emotion. It’s the friendly wave, nod, or blinker switch of acknowledgement, which are the type of nuances the team wants to implement in future work. 

      “The central algorithm of this research is how we can take a dataset and build a completely synthetic world for learning and autonomy,” says Amini. “It’s a platform that I believe one day could extend in many different axes across robotics. Not just autonomous driving, but many areas that rely on vision and complex behaviors. We’re excited to release VISTA 2.0 to help enable the community to collect their own datasets and convert them into virtual worlds where they can directly simulate their own virtual autonomous vehicles, drive around these virtual terrains, train autonomous vehicles in these worlds, and then can directly transfer them to full-sized, real self-driving cars.” 

      Amini and Wang wrote the paper alongside Zhijian Liu, MIT CSAIL PhD student; Igor Gilitschenski, assistant professor in computer science at the University of Toronto; Wilko Schwarting, AI research scientist and MIT CSAIL PhD ’20; Song Han, associate professor at MIT’s Department of Electrical Engineering and Computer Science; Sertac Karaman, associate professor of aeronautics and astronautics at MIT; and Daniela Rus, MIT professor and CSAIL director. The researchers presented the work at the IEEE International Conference on Robotics and Automation (ICRA) in Philadelphia. 

      This work was supported by the National Science Foundation and Toyota Research Institute. The team acknowledges the support of NVIDIA with the donation of the Drive AGX Pegasus. More

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      Ocean vital signs

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      Without the ocean, the climate crisis would be even worse than it is. Each year, the ocean absorbs billions of tons of carbon from the atmosphere, preventing warming that greenhouse gas would otherwise cause. Scientists estimate about 25 to 30 percent of all carbon released into the atmosphere by both human and natural sources is absorbed by the ocean.

      “But there’s a lot of uncertainty in that number,” says Ryan Woosley, a marine chemist and a principal research scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at MIT. Different parts of the ocean take in different amounts of carbon depending on many factors, such as the season and the amount of mixing from storms. Current models of the carbon cycle don’t adequately capture this variation.

      To close the gap, Woosley and a team of other MIT scientists developed a research proposal for the MIT Climate Grand Challenges competition — an Institute-wide campaign to catalyze and fund innovative research addressing the climate crisis. The team’s proposal, “Ocean Vital Signs,” involves sending a fleet of sailing drones to cruise the oceans taking detailed measurements of how much carbon the ocean is really absorbing. Those data would be used to improve the precision of global carbon cycle models and improve researchers’ ability to verify emissions reductions claimed by countries.

      “If we start to enact mitigation strategies—either through removing CO2 from the atmosphere or reducing emissions — we need to know where CO2 is going in order to know how effective they are,” says Woosley. Without more precise models there’s no way to confirm whether observed carbon reductions were thanks to policy and people, or thanks to the ocean.

      “So that’s the trillion-dollar question,” says Woosley. “If countries are spending all this money to reduce emissions, is it enough to matter?”

      In February, the team’s Climate Grand Challenges proposal was named one of 27 finalists out of the almost 100 entries submitted. From among this list of finalists, MIT will announce in April the selection of five flagship projects to receive further funding and support.

      Woosley is leading the team along with Christopher Hill, a principal research engineer in EAPS. The team includes physical and chemical oceanographers, marine microbiologists, biogeochemists, and experts in computational modeling from across the department, in addition to collaborators from the Media Lab and the departments of Mathematics, Aeronautics and Astronautics, and Electrical Engineering and Computer Science.

      Today, data on the flux of carbon dioxide between the air and the oceans are collected in a piecemeal way. Research ships intermittently cruise out to gather data. Some commercial ships are also fitted with sensors. But these present a limited view of the entire ocean, and include biases. For instance, commercial ships usually avoid storms, which can increase the turnover of water exposed to the atmosphere and cause a substantial increase in the amount of carbon absorbed by the ocean.

      “It’s very difficult for us to get to it and measure that,” says Woosley. “But these drones can.”

      If funded, the team’s project would begin by deploying a few drones in a small area to test the technology. The wind-powered drones — made by a California-based company called Saildrone — would autonomously navigate through an area, collecting data on air-sea carbon dioxide flux continuously with solar-powered sensors. This would then scale up to more than 5,000 drone-days’ worth of observations, spread over five years, and in all five ocean basins.

      Those data would be used to feed neural networks to create more precise maps of how much carbon is absorbed by the oceans, shrinking the uncertainties involved in the models. These models would continue to be verified and improved by new data. “The better the models are, the more we can rely on them,” says Woosley. “But we will always need measurements to verify the models.”

      Improved carbon cycle models are relevant beyond climate warming as well. “CO2 is involved in so much of how the world works,” says Woosley. “We’re made of carbon, and all the other organisms and ecosystems are as well. What does the perturbation to the carbon cycle do to these ecosystems?”

      One of the best understood impacts is ocean acidification. Carbon absorbed by the ocean reacts to form an acid. A more acidic ocean can have dire impacts on marine organisms like coral and oysters, whose calcium carbonate shells and skeletons can dissolve in the lower pH. Since the Industrial Revolution, the ocean has become about 30 percent more acidic on average.

      “So while it’s great for us that the oceans have been taking up the CO2, it’s not great for the oceans,” says Woosley. “Knowing how this uptake affects the health of the ocean is important as well.” More

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      Meet the 2021-22 Accenture Fellows

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      Launched in October of 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology come together to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.

      This year’s Accenture Fellows work across disciplines including robotics, manufacturing, artificial intelligence, and biomedicine. Their research covers a wide array of subjects, including: advancing manufacturing through computational design, with the potential to benefit global vaccine production; designing low-energy robotics for both consumer electronics and the aerospace industry; developing robotics and machine learning systems that may aid the elderly in their homes; and creating ingestible biomedical devices that can help gather medical data from inside a patient’s body.

      Student nominations from each unit within the School of Engineering, as well as from the four other MIT schools and the MIT Schwarzman College of Computing, were invited as part of the application process. Five exceptional students were selected as fellows in the initiative’s second year.

      Xinming (Lily) Liu is a PhD student in operations research at MIT Sloan School of Management. Her work is focused on behavioral and data-driven operations for social good, incorporating human behaviors into traditional optimization models, designing incentives, and analyzing real-world data. Her current research looks at the convergence of social media, digital platforms, and agriculture, with particular attention to expanding technological equity and economic opportunity in developing countries. Liu earned her BS from Cornell University, with a double major in operations research and computer science.

      Caris Moses is a PhD student in electrical engineering and computer science specializing inartificial intelligence. Moses’ research focuses on using machine learning, optimization, and electromechanical engineering to build robotics systems that are robust, flexible, intelligent, and can learn on the job. The technology she is developing holds promise for industries including flexible, small-batch manufacturing; robots to assist the elderly in their households; and warehouse management and fulfillment. Moses earned her BS in mechanical engineering from Cornell University and her MS in computer science from Northeastern University.

      Sergio Rodriguez Aponte is a PhD student in biological engineering. He is working on the convergence of computational design and manufacturing practices, which have the potential to impact industries such as biopharmaceuticals, food, and wellness/nutrition. His current research aims to develop strategies for applying computational tools, such as multiscale modeling and machine learning, to the design and production of manufacturable and accessible vaccine candidates that could eventually be available globally. Rodriguez Aponte earned his BS in industrial biotechnology from the University of Puerto Rico at Mayaguez.

      Soumya Sudhakar SM ’20 is a PhD student in aeronautics and astronautics. Her work is focused on theco-design of new algorithms and integrated circuits for autonomous low-energy robotics that could have novel applications in aerospace and consumer electronics. Her contributions bring together the emerging robotics industry, integrated circuits industry, aerospace industry, and consumer electronics industry. Sudhakar earned her BSE in mechanical and aerospace engineering from Princeton University and her MS in aeronautics and astronautics from MIT.

      So-Yoon Yang is a PhD student in electrical engineering and computer science. Her work on the development of low-power, wireless, ingestible biomedical devices for health care is at the intersection of the medical device, integrated circuit, artificial intelligence, and pharmaceutical fields. Currently, the majority of wireless biomedical devices can only provide a limited range of medical data measured from outside the body. Ingestible devices hold promise for the next generation of personal health care because they do not require surgical implantation, can be useful for detecting physiological and pathophysiological signals, and can also function as therapeutic alternatives when treatment cannot be done externally. Yang earned her BS in electrical and computer engineering from Seoul National University in South Korea and her MS in electrical engineering from Caltech. More

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      MIT welcomes nine MLK Visiting Professors and Scholars for 2021-22

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      In its 31st year, the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program will host nine outstanding scholars from across the Americas. The flagship program honors the life and legacy of Martin Luther King Jr. by increasing the presence and recognizing the contributions of underrepresented minority scholars at MIT. Throughout the year, the cohort will enhance their scholarship through intellectual engagement with the MIT community and enrich the cultural, academic, and professional experience of students.

      The 2021-22 scholars

      Sanford Biggers is an interdisciplinary artist hosted by the Department of Architecture. His work is an interplay of narrative, perspective, and history that speaks to current social, political, and economic happenings while examining their contexts. His diverse practice positions him as a collaborator with the past through explorations of often-overlooked cultural and political narratives from American history. Through collaboration with his faculty host, Brandon Clifford, he will spend the year contributing to projects with Architecture; Art, Culture and Technology; the Transmedia Storytelling initiatives; and community workshops and engagement with local K-12 education.

      Kristen Dorsey is an assistant professor of engineering at Smith College. She will be hosted by the Program in Media Arts and Sciences at the MIT Media Lab. Her research focuses on the fabrication and characterization of microscale sensors and microelectromechanical systems. Dorsey tries to understand “why things go wrong” by investigating device reliability and stability. At MIT, Dorsey is interested in forging collaborations to consider issues of access and equity as they apply to wearable health care devices.

      Omolola “Lola” Eniola-Adefeso is the associate dean for graduate and professional education and associate professor of chemical engineering at the University of Michigan. She will join MIT’s Department of Chemical Engineering (ChemE). Eniola-Adefeso will work with Professor Paula Hammond on developing electrostatically assembled nanoparticle coatings that enable targeting of specific immune cell types. A co-founder and chief scientific officer of Asalyxa Bio, she is interested in the interactions between blood leukocytes and endothelial cells in vessel lumen lining, and how they change during inflammation response. Eniola-Adefeso will also work with the Diversity in Chemical Engineering (DICE) graduate student group in ChemE and the National Organization of Black Chemists and Chemical Engineers.

      Robert Gilliard Jr. is an assistant professor of chemistry at the University of Virginia and will join the MIT chemistry department, working closely with faculty host Christopher Cummins. His research focuses on various aspects of group 15 element chemistry. He was a founding member of the National Organization of Black Chemists and Chemical Engineers UGA section, and he has served as an American Chemical Society (ACS) Bridge Program mentor as well as an ACS Project Seed mentor. Gilliard has also collaborated with the Cleveland Public Library to expose diverse young scholars to STEM fields.

      Valencia Joyner Koomson ’98, MNG ’99 will return for the second semester of her appointment this fall in MIT’s Department of Electrical Engineering and Computer Science. Based at Tufts University, where she is an associate professor in the Department of Electrical and Computer Engineering, Koomson has focused her research on microelectronic systems for cell analysis and biomedical applications. In the past semester, she has served as a judge for the Black Alumni/ae of MIT Research Slam and worked closely with faculty host Professor Akintunde Akinwande.

      Luis Gilberto Murillo-Urrutia will continue his appointment in MIT’s Environmental Solutions Initiative. He has 30 years of experience in public policy design, implementation, and advocacy, most notably in the areas of sustainable regional development, environmental protection and management of natural resources, social inclusion, and peace building. At MIT, he has continued his research on environmental justice, with a focus on carbon policy and its impacts on Afro-descendant communities in Colombia.

      Sonya T. Smith was the first female professor of mechanical engineering at Howard University. She will join the Department of Aeronautics and Astronautics at MIT. Her research involves computational fluid dynamics and thermal management of electronics for air and space vehicles. She is looking forward to serving as a mentor to underrepresented students across MIT and fostering new research collaborations with her home lab at Howard.

      Lawrence Udeigwe is an associate professor of mathematics at Manhattan College and will join MIT’s Department of Brain and Cognitive Sciences. He plans to co-teach a graduate seminar course with Professor James DiCarlo to explore practical and philosophical questions regarding the use of simulations to build theories in neuroscience. Udeigwe also leads the Lorens Chuno group; as a singer-songwriter, his work tackles intersectionality issues faced by contemporary Africans.

      S. Craig Watkins is an internationally recognized expert in media and a professor at the University of Texas at Austin. He will join MIT’s Institute for Data, Systems, and Society to assist in researching the role of big data in enabling deep structural changes with regard to systemic racism. He will continue to expand on his work as founding director of the Institute for Media Innovation at the University of Texas at Austin, exploring the intersections of critical AI studies, critical race studies, and design. He will also work with MIT’s Center for Advanced Virtuality to develop computational systems that support social perspective-taking.

      Community engagement

      Throughout the 2021-22 academic year, MLK professors and scholars will be presenting their research at a monthly speaker series. Events will be held in an in-person/Zoom hybrid environment. All members of the MIT community are encouraged to attend and hear directly from this year’s cohort of outstanding scholars. To hear more about upcoming events, subscribe to their mailing list.

      On Sept. 15, all are invited to join the Institute Community and Equity Office in welcoming the scholars to campus by attending a welcome luncheon. More