岭南学术交流会（商务管理系）

报告题目：Optimization with Objective Functions Learned Through Machine Learning

报  告  人：Teng Huang（School of Business, University of Connecticut, Ph.D. Candidate）

主  持  人：王杉（中山大学岭南学院 助理教授）

时       间： 2020年4月30日（周四）上午9:00-10:30

地       点： Zoom ID: 664 3603 3134 

                   https://cernet.zoom.com.cn/j/66436033134

语       言： 英文+中文

Abstract:

My research explores how commonly used machine learning models can be embedded in an optimization problem. In this talk we discuss a novel data-driven decision-making pipeline for solving optimization problems where the objective function is modeled through machine learning. We solve such a decision-making problem in cases when the values of features of a machine learning model depend on the values of decision variables in an optimization model. We explore two real-world applications of this framework in the context of retail network expansion and team formation. In addition, we describe JANOS, an open-source modeling framework that allows users to incorporate linear regression, logistic regression, and neural networks into decision models.

Bio:

Teng Huang is a PhD candidate in the Department of Operations and Information Management at the University of Connecticut School of Business. Her research focuses on integrating machine learning and optimization to facilitate large-scale automated decision-making, and also on quantum computing. She has a paper published at Production and Operations Management and manuscripts under review at INFORMS Journal on Computing and INFORMS Journal on Optimization.

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