September 23, 2020

Costis Daskalakis, MIT

Three ways Machine Learning fails and what to do about them

Constantinos (aka “Costis”) Daskalakis is a Professor of Electrical Engineering and Computer Science at MIT. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, and a PhD. in Electrical Engineering and Computer Science from UC Berkeley, advised by Christos Papadimitriou. He works on Computation Theory and its interface with Game Theory, Economics, Probability Theory, Machine Learning and Statistics. He has been honored with the ACM Doctoral Dissertation Award, the Kalai Prize from the Game Theory Society, the Sloan Fellowship in Computer Science, the SIAM Outstanding Paper Prize, the Microsoft Research Faculty Fellowship, the Simons Investigator Award, the Rolf Nevanlinna Prize from the International Mathematical Union, the ACM Grace Murray Hopper Award, and the Bodossaki Foundation Distinguished Young Scientists Award.

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A common assumption in machine learning and statistics is the existence of training data comprising independent observations from the entire distribution of relevant data. In practice, data deviates from this assumption in various ways. Data might be biased samples from the distribution of interest, due to systematic selection bias, societal biases, incorrect experimental design, or legal restrictions that might prevent the use of all available data. Moreover, observations might be collected on a social network, a spatial or a temporal domain and may thus not be independent but intricately dependent. Finally, data might be affected by the choices made by other learning agents who are learning and making decisions in the same environment where our data is collected and must be acted upon. In the presence of these deviations from the standard i.i.d. model naively trained models fail. In this talk, we overview recent work with various collaborators suggesting avenues to address the resulting challenges through a combined approach involving tools from truncated statistics, high-dimensional probability, statistical physics, and game theory.

October 07, 2020

Meredith Ringel-Morris, Microsoft Research

Distinguished Lecture - Meredith Ringel-Morris

Meredith Ringel-Morris is a computer scientist conducting research in the areas of human-computer interaction (HCI), computer-supported cooperative work (CSCW), social computing, and accessibility. Her current research focus is on accessibility, particularly on the intersection of accessibility and social technologies. In the past, her main research focus was social and collaborative web search. Ringel-Morris has also studied other classes of collaboration technologies, including gesture systems and tabletop computing systems. Ringel-Morris is a Sr. Principal Researcher and Research Manager for the Ability research group at Microsoft Research. She is also an affiliate Professor in The Paul G. Allen School of Computer Science & Engineering and in The Information School at the University of Washington, where I participate in the dub research consortium.

October 19, 2020

Yejin Choi, University of Washington

Distinguished Lecture - Yejin Choi

Yejin Choi is an associate professor of the Paul G. Allen School of Computer Science & Engineering at the University of Washington with the Brett Helsel Career Development Professorship, adjunct of the Linguistics department, and affiliate of the Center for Statistics and Social Sciences. She is also a senior research manager at the Allen Institute for Artificial Intelligence overseeing the project Mosaic on Commonsense Intelligence. Choi is a co-recipient of the AAAI Outstanding Paper Award in 2020, the Marr Prize (best paper award) at ICCV 2013, a recipient of Borg Early Career Award (BECA) in 2018, and named among IEEE AI's 10 to Watch in 2016. She received her PhD in Computer Science at Cornell University and BS in Computer Science and Engineering at Seoul National University in Korea.

November 04, 2020

Joan Feigenbaum, Yale

Distinguished Lecture - Joan Feigenbaum

Joan Feigenbaum is the Grace Murray Hopper Professor of Computer Science at Yale University, where she also holds a courtesy appointment as Professor of Economics. She received a BA in Mathematics from Harvard and a Ph.D. in Computer Science from Stanford. Between finishing her Ph.D. in 1986 and starting at Yale in 2000, she was with AT&T, where she participated broadly in the company's Information-Sciences research agenda, e.g., by creating a research group in Algorithms and Distributed Data, of which she was the manager in 1998-99. Professor Feigenbaum's research interests include security, privacy, anonymity, and accountability; Internet algorithmics; and computational complexity. While at Yale, she has been a principal in several high-profile activities, including the DHS-funded Pri-Fi Project, the DARPA-funded DISSENT project, and the NSF-funded PORTIA project. Her many service contributions to the research community include Program Chair of Crypto '91, Editor-in-Chief of the Journal of Cryptology (1997-2002), Program Co-Chair of the ACM Conference on Electronic Commerce (2004), Program Chair of the ACM Symposium on Theory of Computing (2013), Department Chair of the Yale Computer Science Department (July 2014 through June 2017), General Chair of the inaugural ACM Symposium on Computer Science and Law (2019), and ACM Vice President (July 2020 through June 2022). Professor Feigenbaum is an Amazon Scholar, a Fellow of the ACM, a Fellow of the AAAS, a Member of the Connecticut Academy of Science and Engineering, and a Connecticut Technology Council Woman of Innovation. In 1998, she was an invited speaker at the International Congress of Mathematicians. In May 2020, she won the Test-of-Time Award from the IEEE Symposium on Security and Privacy for her 1996 paper (with Matt Blaze and Jack Lacy) entitled "Decentralized Trust Management."

November 09, 2020


Distinguished Lecture - Joel Emer

Joel Emer is a Professor of the Practice at MIT's Electrical Engineering and Computer Science Department (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a Senior Distinguished Research Scientist at Nvidia in Westford, MA, where he is responsible for exploration of future architectures as well as modeling and analysis methodologies. Prior to joining NVIDIA, he worked at Intel where he was an Intel Fellow and Director of Microarchitecture Research. Previously he worked at Compaq and Digital Equipment Corporation (DEC). Dr. Emer has held various research and advanced development positions investigating processor micro-architecture and developing performance modeling and evaluation techniques. He has made architectural contributions to a number of VAX, Alpha and X86 processors and is recognized as one of the developers of the widely employed quantitative approach to processor performance evaluation. He has also been recognized for his contributions in the advancement of simultaneous multi-threading technology, analysis of the architectural impact of soft errors, memory dependence prediction, pipeline and cache organization, performance modeling methodologies and spatial architectures. Dr. Emer holds over 25 patents and has published more than 60 papers.

November 16, 2020

Susan Landau, Tufts

Distinguished Lecture - Susan Landau

Susan Landau works at the intersection of cyber security, national security, law, and policy. She has testified before Congress, written for the Washington Post, Science, and Scientific American, and frequently appears on NPR and BBC. Her previous positions include senior staff privacy analyst at Google, distinguished engineer at Sun Microsystems, and faculty member at Worcester Polytechnic Institute, the University of Massachusetts Amherst, and Wesleyan University.

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