Matrix computations are ubiquitous in scientific computing and data science. However, many existing methods consume enormous computational resources for solving increasingly large and complex problems. In this talk, I will describe my work on exploiting mathematical structures and hardware capabilities to accelerate matrix computations. I will focus on a randomized algorithm, namely RChol, for computing an incomplete Cholesky factorization of a graph Laplacian, which arises from data clustering, semi-supervised learning, and the solution of partial differential equations. RChol employs a randomized sampling scheme developed by Spielman and Kyng to prevent excessive fill-in introduced by Gaussian elimination. Compared to its deterministic counterparts, RChol delivers faster convergence, less running time, and better parallel scalability.

Chao Chen has been an assistant professor in the Department of Mathematics since 2023. Before that, he was a postdoctoral fellow in the Oden Institute at The University of Texas at Austin. He received his PhD from the Institute for Computational and Mathematical Engineering (ICME) at Stanford in 2018.

His research generally focuses on developing efficient algorithms for matrix computations with applications to computational tasks ranging from solving partial differential equations to analyzing large high-dimensional datasets.

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Meeting ID: 934 4558 2296
Passcode: 051656