MIT Stochastics & Statistics Seminar Series: Roman Vershynin
Title: Discovering hidden structures in complex networks
Abstract: Most big real-world networks (social, technological, biological) are sparse. Most of networks have noticeable structure, which can be formed by clusters (communities) and hubs. When and how can a hidden structure be recovered from a sparse network? Known approaches to this problem come from a variety of disciplines — probability, combinatorics, physics, statistics, optimization, information theory, etc. We will focus on the recently developed probabilistic approaches motivated by sparse recovery, where a network is regarded as a random measurement of the hidden structure.
Bio: Roman Vershynin is a Professor of Mathematics working at the University of Michigan. His primary area of expertise is high dimensional probability. He is interested in random geometric structures that appear across mathematics and data science, in particular random matrix theory, geometric functional analysis, convex and discrete geometry, geometric combinatorics, high dimensional statistics, information theory, learning theory, signal processing, numerical analysis, and network science.
Roman Vershynin received an equivalent of M.S. from Kharkiv National University in Ukraine in 1996, and Ph.D. from University of Missouri-Columbia in 2000. Prior to his appointment at the University of Michigan, he was a faculty at the University of California, Davis (2003-2008) and a postdoctoral fellow at the University of Alberta in Canada (2001-2003) and Weizmann Institute of Science in Israel (2000-2001). He was a recipient of the Alfred Sloan Research Fellowship in 2005, Bessel Research Award from Humboldt Foundation in 2013, an invited speaker at the International Congress of Mathematicians in 2010, and a plenary speaker at the Summer Meeting of the Canadian Mathematical Society in 2011.
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