CCMS Colloquium invites you to a talk by Anna Ma (UCI)
Title: Stochastic iterative methods for solving tensor linear systems
Abstract: Solving linear systems is a crucial subroutine and challenge in data science and scientific computing. Classical approaches for solving linear systems assume that data is readily available and small enough to be stored in memory. However, in the large-scale data setting, data may be so large that only partitions (e.g., single rows/columns of the matrix/tensor) can be utilized at a time. In this presentation, we discuss the advantages and role of randomization in iterative methods for approximating the solution to large-scale linear systems. Time permitting, we will also discuss our recent work on applications to solving systems involving higher-dimensional arrays, or tensors. Unlike previously proposed randomized iterative strategies, such as the tensor randomized Kaczmarz method (row slice method) or the tensor Gauss-Seidel method (column slice method), which are natural extensions of their matrix counterparts, our approach delves into a distinct scenario utilizing frontal slice sketching.
Bio: Dr. Anna Ma is an Assistant Professor at UC Irvine in the Department of Mathematics. Prior to her position at UCI, she was a Visiting Assistant Professor at UCI and a UC Chancellor’s Postdoctoral Fellow at UC San Diego in the Department of Mathematics. Her research interests are in randomized algorithms, numerical linear algebra, and the mathematics of data science. She is also interested in data visualization and unsupervised machine learning. Anna earned her BS in Mathematics at UC Los Angeles. She received her PhD in Computational Science from Claremont Graduate University and the Computational Science Research Center at San Diego State University, where she studied the design and analysis of algorithms that solve problems involving large-scale data.
