March 24-26, 2021
Watch Workshop Videos: C3.ai DTI YouTube Channel (From our homepage, scroll down to Workshops)
This three-day workshop will explore the confluence of four major elements: machine learning algorithms, platforms, user populations, and regulatory/market structures, in large-scale socio-technical systems. Specifically, we will examine this confluence in the context of three domains: social media and content moderation, transportation and mobility, and healthcare delivery systems.
Each day focuses on one of these domains and brings together three experts who will each give a brief position talk and then take part in a panel discussion that involves answering questions from the audience.
Saurabh Amin (Massachusetts Institute of Technology), Aleksander Madry (Massachusetts Institute of Technology), Asu Ozdaglar (Massachusetts Institute of Technology)
Lars Backstrom (Facebook), Alexandre Bayen (University of California, Berkeley), Marzyeh Ghassemi (University of Toronto), Matthew Jackson (Stanford University), Karl Johansson (KTH Royal Institute of Technology), Jon Kleinberg (Cornell University), Aleksander Madry (Massachusetts Institute of Technology), Sendhil Mullainathan (University of Chicago), Asu Ozdaglar (Massachusetts Institute of Technology), Shankar Sastry (University of California, Berkeley), Michael Schwarz (Microsoft)
(All times are Pacific Time)
Asu Ozdaglar (Massachusetts Institute of Technology)
Session I: Social Media and Content Moderation
Moderator: Aleksander Madry (Massachusetts Institute of Technology)
11:45 am – 12 pm: Social Network Structure and Impediments to Learning, Matthew O. Jackson (Stanford University)
Speaker: Matthew O. Jackson is the William D. Eberle Professor of Economics at Stanford University and an External Faculty member of the Santa Fe Institute. His research interests include game theory, microeconomic theory, and the study of social and economic networks, on which he has published many articles and the books The Human Network and Social and Economic Networks. He also teaches an online course on networks and co-teaches two others on game theory. Jackson is a Member of the National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, a Fellow of the Econometric Society, a Game Theory Society Fellow, and an Economic Theory Fellow. Among his many honors is a Guggenheim Fellowship, the Social Choice and Welfare Prize, the von Neumann Award from Rajk Laszlo College, an honorary doctorate from Aix-Marseille University, the B.E. Press Arrow Prize for Senior Economists, and teaching awards. He is the President of the Game Theory Society.
12 pm – 12:15 pm: The Interplay Between Social and Adversarial Effects in Online Information, Jon Kleinberg (Cornell University)
Speaker: Jon Kleinberg is the Tisch University Professor at Cornell University. His research focuses on the interaction of algorithms and networks and the roles they play in large-scale social and information systems. His work has been supported by an NSF CAREER Award, an ONR Young Investigator Award, a MacArthur Foundation Fellowship, a Packard Foundation Fellowship, a Simons Investigator Award, a Sloan Foundation Fellowship, a Vannevar Bush Faculty Fellowship, and grants from Facebook, Google, Yahoo, the MacArthur Foundation, the ARO, and the NSF. Kleinberg is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.
12:15 pm – 12:30 pm: News Feed Ranking and Network Effects, Lars Backstrom (Facebook)
Speaker: Lars Backstrom is a Vice President of Engineering at Facebook. He has been in that role since August 2013 and at Facebook since 2009. Backstrom built the backend infrastructure for the “People You May Know” feature and manages the News Feed and Stories engineering teams. Backstrom obtained his Ph.D. in computer science from Cornell University in 2009 where he worked in data mining and machine learning using large-scale datasets, with an emphasis on web information, social computing applications, and online social networks.
12:30 pm – 12:40 pm: Break
12:40 pm – 2 pm: Discussion and Q&A
Session II: Transportation and Mobility Platforms
Moderator: Claire Tomlin (University of California, Berkeley)
11:30 am – 11:45 am: Control of Mixed-Autonomy Traffic via Deep-RL, Alexandre Bayen (University of California, Berkeley)
Speaker: Alexandre Bayen is the Liao-Cho Professor of Engineering at the University of California, Berkeley. He is the Director of the Institute of Transportation Studies and a Faculty Scientist in Mechanical Engineering at the Lawrence Berkeley National Laboratory. Bayen has authored two books and over 200 articles in peer-reviewed journals and conferences. He is the recipient of the Ballhaus Award from Stanford University, 2004, the CAREER award from the National Science Foundation in 2009, and was named a NASA Top 10 Innovators on Water Sustainability in 2010. His projects Mobile Century and Mobile Millennium received the 2008 Best of ITS Award for Best Innovative Practice at the ITS World Congress and a TRANNY Award from the California Transportation Foundation in 2009. He is the recipient of the PECASE Award from the White House in 2010, the Okawa Research Grant Award, the Ruberti Prize from the IEEE, and the Huber Prize from the ASCE.
11:45 am – 12 pm: How to Automate Road Freight Transport and its Consequence for Traffic Conditions, Karl H. Johansson (KTH Royal Institute of Technology)
Speaker: Karl H. Johansson is a Professor at the School of EECS and Director of Digital Futures at KTH Royal Institute of Technology, Sweden. His research interests are in networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. He is a member of the Swedish Research Council’s Scientific Council for Natural Sciences and Engineering Sciences. He has served on the IEEE Control Systems Society Board of Governors, the IFAC Executive Board, and is currently Vice President of the European Control Association. He has received several best paper awards and other distinctions from IEEE, IFAC, and ACM. He has been awarded a Distinguished Professor with the Swedish Research Council and a Wallenberg Scholar with the Knut and Alice Wallenberg Foundation. He has received the Future Research Leader Award from the Swedish Foundation for Strategic Research and the triennial Young Author Prize from IFAC. He is Fellow of the IEEE and the Royal Swedish Academy of Engineering Sciences, and he is IEEE Control Systems Society Distinguished Lecturer.
12 pm – 12:15 pm: The Future of Transportation: AVs, Tolls, Carpools, and Remote Driving, Michael Schwarz (Microsoft)
Speaker: Michael Schwarz is a Corporate Vice President and Chief Economist at Microsoft. His office of economists and data scientists engages primarily in three areas: 1) Demand Forecasting to optimize capacity planning/supply chain management and to identify top trends, 2) Product Pricing and Discounting (e.g., Azure Services, Surface, Windows), and 3) Market Design to optimize the effectiveness of marketplaces. Schwarz drives key topics and themes that need cross-company economics representation. Before joining Microsoft, Schwarz worked at Google, where he was most recently Chief Economist for Google Cloud. Prior to that, Schwarz led the team that built the carpooling marketplace Waze Carpool, where he subsequently became Chief Scientist for Waze. Before his tenure at Google, Schwarz was the head of the economics research unit at Yahoo! where he played a critical role in designing ad marketplaces.
12:15 pm – 12:30 pm: Break
12:30 pm – 2 pm: Discussion
Session III: Healthcare Delivery Systems
Moderator: Shankar Sastry (University of California, Berkeley)
11:30 am – 11:45 am: Don’t Expl-AI-n Yourself, Marzyeh Ghassemi (University of Toronto)
Speaker: Marzyeh Ghassemi is an Assistant Professor in Computer Science and Medicine and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair at the University of Toronto. Ghassemi will join MIT’s IMES/EECS in July 2021. Prior to her PhD in Computer Science at MIT, Ghassemi received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. She has a well-established academic track record across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, EMBC, Nature Medicine, Nature Translational Psychiatry, and Critical Care.
11:45 am – 12 pm: Healthcare’s Buggy Data Problem, Sendhil Mullainathan (University of Chicago)
Speaker: Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at the University of Chicago Booth School of Business. His research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. In past work, he has combined insights from economics and behavioral science with causal inference tools — lab, field, and natural experiments — to study social problems such as discrimination and poverty. Prior to joining Booth, Mullainathan was the Robert C. Waggoner Professor of Economics in the Faculty of Arts and Sciences at Harvard University, where he taught courses about machine learning and big data. Mullainathan is a recipient of a MacArthur Fellowship and began his academic career at MIT.
12 pm – 12:15 pm: Machine Learning for Clinical Decision-Making: Panacea or Pandora’s Box, Collin Stultz (Massachusetts Institute of Technology)
Speaker: Collin M. Stultz is a Professor of Electrical Engineering and Computer Science, a faculty member in the Harvard-MIT Division of Health Sciences and Technology, a Professor in the Institute of Medical Engineering and Sciences, a member of the Research Laboratory of Electronics (RLE), and an associate member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), all at the Massachusetts Institute of Technology, and a practicing cardiologist at the Massachusetts General Hospital. He received his undergraduate degree in Mathematics and Philosophy and a PhD in Biophysics, both from Harvard University, an MD from Harvard Medical School, and did his internship, residency, and fellowship at the Brigham and Women’s Hospital in Boston. His scientific contributions span the fields of computational chemistry, biophysics, and machine learning for cardiovascular risk stratification and his current research focuses on developing machine learning tools to guide clinical decision making. He is a member of the American Society for Biochemistry and Molecular Biology and the Federation of American Societies for Experimental Biology and he is a past recipient of a National Science Foundation CAREER Award and a Burroughs Wellcome Fund Career Award in the Biomedical Sciences.
12:15 pm – 12:20 pm: Break
12:20 pm – 1:50 pm: Discussion