Private Event
An Invitation to Computational Epidemiology
Speaker
Ankur Moitra and Elchanan Mossel
CSAIL MIT
Host
Daniela Rus
CSAIL
Join Zoom: https://mit.zoom.us/j/475800555
One tap mobile +16465588656,,475800555# US (New York)
Meeting ID: 475 800 555
Abstract:
Many researchers, us included, have been interested in the mathematical, statistical and computational problems in epidemiology. However with the outbreak of COVID-19, these problems have taken on a completely different level of urgency. In this talk we will introduce some of the basic problems in computational epidemiology, and will open up the discussion to ways that computational thinking can contribute.
Bios:
Ankur Moitra is an assistant professor in the Department of Mathematics at MIT and a member of the Computer Science and Artificial Intelligence Lab (CSAIL). Prior to that, he was an NSF CI Fellow at the Institute for Advanced Study, and also a senior postdoc in the computer science department at Princeton University. He completed his PhD and MS at MIT in 2011 and 2009 respectively, where he was advised by Tom Leighton and was supported by a Fannie and John Hertz Foundation Fellowship. He received a George M. Sprowls Award (best thesis) and a William A. Martin Award (best thesis) for his doctoral and master's dissertations. He has worked in numerous areas of algorithms, including approximation algorithms, metric embeddings, combinatorics and smoothed analysis, but lately has been working at the intersection of algorithms and machine learning.
Elchanan Mossel works in probability, combinatorics and inference. His interests include combinatorial statistics, discrete Fourier analysis, randomized algorithms, computational complexity, Markov random fields, social choice, game theory, evolution and the mathematical foundations of deep learning. His research in discrete function inequalities, isoperimetry and hypercontractiviting led to the proof that Majority is Stablest and confirmed that optimality of the Goemans-Williamson MAX-CUT algorithm under the unique games conjecture from computational complexity. His research has resolved open problems in computational biology, machine learning, social choice theory and economics. Elchanan Mossel received the B.Sc. from The Open University in Israel in 1992. He received both the M.Sc. (1997) and Ph.D. (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a post-doctoral fellow at the Microsoft Research Theory Group and a Miller Fellow at U.C. Berkeley. He joined the U.C. Berkeley faculty in 2003 where he was a professor of statistics and computer science. Prof Mossel is on the senior faculty of the Mathematics Department, with a jointly core faculty appointment at the Statistics and Data Science Center of MIT’s Institute for Data, Systems and Society (IDSS).
One tap mobile +16465588656,,475800555# US (New York)
Meeting ID: 475 800 555
Abstract:
Many researchers, us included, have been interested in the mathematical, statistical and computational problems in epidemiology. However with the outbreak of COVID-19, these problems have taken on a completely different level of urgency. In this talk we will introduce some of the basic problems in computational epidemiology, and will open up the discussion to ways that computational thinking can contribute.
Bios:
Ankur Moitra is an assistant professor in the Department of Mathematics at MIT and a member of the Computer Science and Artificial Intelligence Lab (CSAIL). Prior to that, he was an NSF CI Fellow at the Institute for Advanced Study, and also a senior postdoc in the computer science department at Princeton University. He completed his PhD and MS at MIT in 2011 and 2009 respectively, where he was advised by Tom Leighton and was supported by a Fannie and John Hertz Foundation Fellowship. He received a George M. Sprowls Award (best thesis) and a William A. Martin Award (best thesis) for his doctoral and master's dissertations. He has worked in numerous areas of algorithms, including approximation algorithms, metric embeddings, combinatorics and smoothed analysis, but lately has been working at the intersection of algorithms and machine learning.
Elchanan Mossel works in probability, combinatorics and inference. His interests include combinatorial statistics, discrete Fourier analysis, randomized algorithms, computational complexity, Markov random fields, social choice, game theory, evolution and the mathematical foundations of deep learning. His research in discrete function inequalities, isoperimetry and hypercontractiviting led to the proof that Majority is Stablest and confirmed that optimality of the Goemans-Williamson MAX-CUT algorithm under the unique games conjecture from computational complexity. His research has resolved open problems in computational biology, machine learning, social choice theory and economics. Elchanan Mossel received the B.Sc. from The Open University in Israel in 1992. He received both the M.Sc. (1997) and Ph.D. (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a post-doctoral fellow at the Microsoft Research Theory Group and a Miller Fellow at U.C. Berkeley. He joined the U.C. Berkeley faculty in 2003 where he was a professor of statistics and computer science. Prof Mossel is on the senior faculty of the Mathematics Department, with a jointly core faculty appointment at the Statistics and Data Science Center of MIT’s Institute for Data, Systems and Society (IDSS).