MIT Schwarzman College of Computing

Frida Polli, PhD, a neuroscientist turned entrepreneur known for her leading-edge contributions at the crossroads of behavioral science and AI, is MIT's next visiting innovation scholar, the first to be hosted by the Schwarzman College of Computing. Polli is collaborating with Sendhil Mullainathan, the Peter de Florez Professor in the departments of Electrical Engineering and Computer Science and Economics, and a principal investigator in the MIT Laboratory for Information and Decision Systems (LIDS), to bring together a broad array of faculty, students, and postdocs across MIT to address concrete problems where humans and algorithms intersect, to develop a new subdomain of computer science specific to behavioral science, and to train the next generation of scientists to be bilingual in these two fields. https://bit.ly/FPolli

A team of researchers in the MIT Jameel Clinic for Machine Learning in Health have released a powerful, open-source AI model, called Boltz-1, that could significantly accelerate biomedical research and drug development. Boltz-1 is the first fully open-source model that achieves state-of-the-art performance at the level of AlphaFold3, the model from Google DeepMind that predicts the 3D structures of proteins and other biological molecules. https://bit.ly/MITBoltz-1

A team of students from around the MIT community, known as SERC Scholars, had the opportunity to co-author a case study about the environmental and climate justice implications of the electronics hardware life cycle. The SERC Scholars' case study was published by the MIT Case Studies in Social and Ethical Responsibilities of Computing. The ongoing series, which releases new issues twice a year on an open-source platform, is enabling undergraduate instructors worldwide to incorporate research-based education materials on computing ethics into their existing class syllabi. “Many students are focused on climate, whether through computer science, data science, or mechanical engineering. I hope that this case study educates them on environmental and climate aspects of e-waste and computing,” says Lelia Hampton, a PhD candidate in electrical engineering and computer science and one of the co-authors of the case study, “From Mining to E-waste: The Environmental and Climate Justice Implications of the Electronics Hardware Life Cycle” Read more the SERC Scholars' case study at https://lnkd.in/ebnMFF4B

Four faculty members from MIT EECS were named to the 2024 cohort of AI2050 Fellows: Sara Beery, Gabriele Farina, Marzyeh Ghassemi, and Yoon Kim. A philanthropic initiative of Schmidt Futures, AI2050 is aimed at helping to solve hard problems in AI. Within their research, each fellow will contend with the central motivating question of: “It’s 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome?” Read more about the fellows at https://bit.ly/AI2050-MIT.

Congratulations to Paul Liang, a shared faculty member between the MIT Media Lab and MIT Schwarzman College of Computing, on being named to Forbes’ 30 Under 30 list in the category of science for 2025. Liang, an assistant professor who joined MIT in September 2024, builds AI systems that learn and interact with the world in new ways. His work has laid the theoretical foundations which enables AI to understand and retrieve multimedia content, infer human intents and navigate social interactions, and even identify humor and sarcasm in conversations. Forbes’ 30 Under 30 is an annual list that recognizes young innovators, disruptors, and visionaries building the future in 20 industries. https://lnkd.in/gnrPZa-Q

A new method developed by MIT researchers uses generative AI to produce sharp, lifelike 3D shapes that are closer in quality to the best model-generated 2D images. “In the long run, our work can help facilitate the process to be a co-pilot for designers, making it easier to create more realistic 3D shapes,” says Artem Lukoianov, an electrical engineering and computer science graduate student who is lead author of a paper on this technique. https://lnkd.in/eBdQ9srv

Marzyeh Ghassemi, associate professor in MIT EECS and MIT Institute for Medical Engineering and Science (IMES) and principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS), works on the deep study of how machine learning can be made more robust and subsequently applied to improve safety and equity in health. Captivated as a child by video games and puzzles, Ghassemi was also fascinated at an early age in health. Luckily, she found a path where she could combine the two interests. “Although I had considered a career in health care, the pull of computer science and engineering was stronger,” says Ghassemi,. “When I found that computer science broadly, and AI/ML specifically, could be applied to health care, it was a convergence of interests.” https://bit.ly/MGhassemi

AI systems are being trained to make decisions across fields like robotics, medicine, and political science. But unfortunately, teaching an AI system to make good decisions is no easy task. Reinforcement learning models, which underlie these AI decision-making systems, still often fail when faced with even small variations in the tasks they are trained to perform. To address this, MIT researchers developed a more efficient algorithm that strategically selects key tasks for training. This approach improves performance on related tasks while reducing training costs. “We were able to see incredible performance improvements, with a very simple algorithm, by thinking outside the box. An algorithm that is not very complicated stands a better chance of being adopted by the community because it is easier to implement and easier for others to understand,” says senior author Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering and the MIT Institute for Data, Systems, and Society (IDSS), and a member of the Laboratory for Information and Decision Systems. https://lnkd.in/ehKStA-P

Sara Beery, an assistant professor of artificial intelligence and decision-making in MIT EECS, combines machine learning with human expertise to better understand a rapidly changing planet. According to the World Wide Fund for Nature, it is estimated that nearly 70% of wild animals have vanished since the 1970’s. With advancements in cameras, satellite imaging, acoustic sensors, drone-based surveys, animal tracking devices and other ecosystem sensing equipment, scientists are collecting a huge explosion of wildlife data that can help to understand this global crisis. The vast majority of that data, however, is sitting in hard drives under someone’s desk untouched. Beery’s research has shown that AI can help in filtering through this enormous flood of ecological data to discover ecosystem trends and biodiversity losses. “We have to figure out how to make use of a combination of expert human intelligence and large-scale, hopefully very robust machine learning systems,” she says. https://bit.ly/SBeery Story: Eric Bender for MIT Industrial Liaison Program (ILP) Photo: David Sella, MIT ILP

As part of the MIT Institute for Data, Systems, and Society (IDSS) Initiative on Combatting Systemic Racism (ICSR), researchers are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care. “There’s extensive research showing racial discrimination and systemic inequity in essentially all sectors of American society,” explains Fotini Christia, the Ford International Professor of Social Sciences in the Department of Political Science, director of IDSS, and co-lead of ICSR. “Newer research demonstrates how computational technologies, typically trained or reliant on historical data, can further entrench racial bias. But these same tools can also help to identify racially inequitable outcomes, to understand their causes and impacts, and even contribute to proposing solutions.” https://lnkd.in/eJVFHS5Z