Professor of Mathematics
Probability, Algorithms and Inference
Elchanan Mossel works in probability, combinatorics and inference. His interests include combinatorical 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 work on the reconstruction problem on trees provides optimal algorithms and bounds for phylogenetic reconstruction in molecular biology and has led to sharp results in the analysis of Gibbs samplers from statistical physics and inference problems on graphs. His research has resolved open problems in computational biology, machine learning, social choice theory, and economics.
Mossel received the BSc from the Open University in Israel in 1992. He received both the MSc (1997) and PhD (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a postdoctoral fellow at the Microsoft Research Theory Group and a Miller Fellow at University of California at Berkeley. He joined the University of California at Berkeley faculty in 2003, where he was a professor of statistics and computer science. He spent leaves as a professor at the Weizmann Institute (2008-2010) and at the Wharton School, University of Pennsylvania (2014-2016). His distinctions include the Sloan Research Fellowship (2005), the NSF CAREER Award (2006), and the Bergmann Memorial Award, the U.S.-Israel Binational Science Foundation (2007). Mossel joined the faculty of the MIT Mathematics Department as a full professor in July 2016, with a joint appointment at the Statistics and Data Science Center of the MIT Institute for Data, Systems and Society.