CENTER FOR COMPUTATION & TECHNOLOGY

Optimization models that incorporate uncertainty and hierarchical structures have attracted much attention in data science. Recent advances in polynomial optimization offer promising methods to certify global optimality for these complex models. In this talk, I will use two-stage stochastic optimization as a major model to demonstrate how polynomial optimization can be efficiently applied to data science optimization under uncertainty.

Dr. Suhan Zhong is a Visiting Assistant Professor in Mathematics at Texas A&M University. She obtained her Ph.D. in Mathematics at UCSD in June 2022.  Dr. Zhong works in optimization with connections to data science. Her work uses techniques from real algebraic geometry, moment problems and optimization theory.