Abstract: Interpolative decomposition is a simple and yet powerful tool for approximating low-rank matrices. In this talk, I will first review the theory and algorithms. Then I will discuss a few new applications of interpolative decomposition in numerical linear algebra, partial differential equations, quantum chemistry, and machine learning.
Bio: Lexing Ying is 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 Medal in Applied Mathematics (2016).