### COLT 2016 Conference On Learning Theory

##### June 17, 2016

The 2016 Conference On Learning Theory will be held in New York City, conveniently located close to ICML, which will take place immediately after.

**Invited speakers:**

**David Donoho** is a professor of statistics at Stanford University, where he is also the Anne T. and Robert M. Bass Professor in the Humanities and Sciences. He has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description. He has made fundamental contributions to the development of wavelets and to compressed sensing. He is a winner of the Shaw Prize in Mathematical Sciences (2013), the Weiner Prize in Applied Mathematics (2010), and a MacArthur Fellowship recipient..

**Ravi Kannan**, Microsoft Research. Before joining Microsoft, Kannan was the William K. Lanman Jr. Professor of Computer Science and Professor of Applied Mathematics at Yale University. He has also taught at CMU and MIT. The ACM Special Interest Group on Algorithms and Computation Theory (SIGACT) presented its 2011 Knuth Prize to Ravi Kannan for “developing influential algorithmic techniques aimed at solving long-standing computational problems.” He is the recipient of the 1992 Fulkerson Prize in Discrete Mathematics. His research interests include Algorithms, Theoretical Computer Science and Discrete Mathematics as well as Optimization. His work has mainly focused on efficient algorithms for problems of a mathematical (often geometric) flavor that arise in Computer Science. He has worked on algorithms for integer programming and the geometry of numbers, random walks in n-space, randomized algorithms for linear algebra and learning algorithms for convex sets.

**Ronitt Rubinfeld** has been at MIT since 2004 and at Tel Aviv University since 2008. Ronitt’s research interests revolve around sublinear time algorithms. Her work focuses on the question of what can be understood about data by looking at only a very small portion of it. Much of her current work also concentrates on testing properties and estimating parameters of distributions over very large domains. Ronitt was a Radcliffe Fellow in 2004, and is a fellow of the ACM.

*— Event producer*