Microsoft Research New England 10th Anniversary Symposium – Panel: Frontiers of Machine Learning

Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multi agent systems. He also applies machine learning to various problems in artificial intelligence,notably the automated design and analysis of algorithms for solving hard computational problems.

Anima Anandkumar is a Bren professor at Computing + Mathematical sciences department at Caltech. Anima Anandkumar’s research interests span theory and practice of large-scale machine learning. In particular, she has been spearheading the development and analysis of multi-dimensional (tensor) algorithms for machine learning. She is the recipient of several awards such as the Bren endowed chair professorship at Caltech, Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young Investigator Awards, NSF Career Award, and several best paper awards. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant professor at U.C. Irvine between 2010 and 2016, a visiting researcher at Microsoft Research New England in 2012 and 2014, and a Principal Scientist at Amazon Web Services between 2016-2018.

Victor Chernozhukov works in econometrics and mathematical statistics, with much of recent work focusing on the quantification of uncertainty in very high dimensional models. He is a fellow of The Econometric Society and a recipient of The Alfred P. Sloan Research Fellowship and The Arnold Zellner Award. He was elected to the American Academy of Arts and Sciences in April 2016.

Nicolo Fusi is a researcher working at the intersection of machine learning, computational biology and medicine. His focus is on the development of new statistical and computational methods to better understand the genetic and environmental causes of complex diseases. In machine learning, his main interest is in the development of scalable inference methods for Bayesian nonparametric models. Recently, he has also been working on sensing using wearable devices and the computational aspects of gene therapy. Nicolo received his PhD in Computer Science from the University of Sheffield working with Neil Lawrence. He received his B.Sc. and M.Sc. in theoretical computer science from the University of Milan.

I’m a researcher at Microsoft Research New England and an adjunct professor of Statistics at Stanford University. I spent three wonderful years as an assistant professor of Statistics and, by courtesy, Computer Science at Stanford and one as a Simons Math+X postdoctoral fellow, working with Emmanuel Candes at Stanford. I received my Ph.D. in Computer Science (2012) and my M.A. in Statistics (2011) from UC Berkeley and my B.S.E. in Computer Science (2007) from Princeton University. My Ph.D. advisor was Mike Jordan, and my undergraduate research advisors were Maria Klawe and David Walker. My current research interests include statistical machine learning, algorithms and data structures, high-dimensional statistics, and concentration inequalities. Lately, I’ve been developing and analyzing scalable learning algorithms for healthcare, recommender systems, approximate posterior inference, high-energy physics, and the social good. Quixotic though it may sound, I hope to use computer science and statistics to change the world for the better. If you have thoughts on how to do this, feel free to contact me.