ECpE Seminar with Shuang Li: Non-convex Optimization in Data Science


November 16, 2022    
10:00 am - 11:30 am


3043 ECpE Building Addition
Coover Hall , Ames

Event Type

Title: Non-convex Optimization in Data Science

Abstract: High-dimensional data analysis and estimation appear in many data science and machine learning applications. The underlying low-dimensional structure in these high-dimensional data inspires us to develop optimality guarantees as well as optimization-based techniques for the fundamental problems in data science and machine learning. In recent years, non-convex optimization widely appears in engineering and is solved by many heuristic local algorithms, but lacks global guarantees. The recent geometric/landscape analysis provides a way to determine whether an iterative algorithm can reach global optimality. The landscape of empirical risk has been widely studied in a series of machine learning problems, including low-rank matrix factorization, matrix sensing, matrix completion, and phase retrieval. A favorable geometry guarantees that many algorithms can avoid saddle points and converge to local minima. In this talk, I will introduce some of our recent work on geometric analysis and stochastic algorithms for non-convex optimization problems.

Bio: Shuang Li received the B.Eng. degree in communications engineering from the Zhejiang University of Technology, Hangzhou, China, in 2013, and the Ph.D. degree in electrical engineering from the Colorado School of Mines, Golden, CO, USA, in 2020. She is currently a Hedrick Assistant Adjunct Professor with the Department of Mathematics, University of California, Los Angeles, CA, USA. Her research interests include developing optimization-based techniques with optimality guarantees for fundamental problems in signal processing, machine learning and data science.