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Interpolative Decomposition and its Applications

Abstract

Interpolative decomposition is a simple and yet powerful tool for approximating low-rank matrices. After discussing the theory and algorithm, I will present a few new applications of interpolative decomposition in numerical partial differential equations, quantum chemistry, and machine learning.


This is a joint Math Department-ICM Colloquium lecture.


Biography

Lexing Ying has been Professor of Mathematics at Stanford University since 2012. Prior to that, he was a professor at the University of Texas at Austin from 2006 to 2012. His research focuses on computational mathematics and scientific computing. He received his Ph.D. from New York University and was a postdoctoral scholar at California Institute of Technology from 2004 to 2006. He is a recipient of the Sloan Research Fellowship (2007), the National Science Foundation CAREER Award (2009), the Feng Kang Prize of Scientific Computing (2011), the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing from SIAM (2013), and the Silver Morningside Medeal in Applied Mathematics (2016).

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