3108 Etcheverry Hall
Abstract – Traditionally, Stochastic Optimization deals with optimization models in which some of the data is modeled using random variables. In contrast, Learning Models are intended to capture the behavior of covariates, where the goal is to characterize the behavior of the response (random variable) to the predictors (random variables). The field of Statistical (or Machine) Learning focuses on understanding these relationships. The goal of this talk is to present a new class of composite optimization models in which the learning and optimization models live symbiotically. We will discuss several examples of such problems, and how they give rise to a rich class of problems. (This talk is based on the work of several Ph.D. students, and in particular Yunxiao Deng, Junyi Liu and Shuotao Diao).
Bio: Professor Sen served as a program director at NSF where he was responsible for the Operations Research, and the Service Enterprise Engineering programs. At NSF, he also headed the Cyber infrastructure planning activities of the Engineering Directorate. Concurrently with his appointment at NSF, he was a professor of Systems and Industrial Engineering at the University of Arizona.
Professor Sen has served on the editorial board of several journals, including Operations Research as Area Editor for Optimization, and as Associate Editor in INFORMS Journal on Computing, Telecommunications Systems, as well as Operations Research. He is the past-Chair of the INFORMS Telecommunications Section and founded the INFORMS Optimization Section. Professor Sen is a Fellow of INFORMS.