ISE Seminar Series

 The Lehigh ISE Seminar Series is a component of the Spencer C. Schantz Lecture Program.

 Upcoming Events

 ISE Seminar Series Spring 2024

 Time: Tuesday, April 16, 11:00 am - 12:00 pm

 Speaker: Weijun Xie, Georgia Tech

 Title: Distributionally Fair Stochastic Optimization using Wasserstein Distance

 Location: Mohler Lab, Room 453

Abstract: 

A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a set of constraints. However, implementing such optimization-based decisions can have varying impacts on individuals, and when assessed using the individuals' utility functions, these impacts may differ substantially across demographic groups delineated by sensitive attributes, such as gender, race, age, and socioeconomic status. As each group comprises multiple individuals, a common remedy is to enforce group fairness, which necessitates the measurement of disparities in the distributions of utilities across different groups. This paper introduces the concept of Distributionally Fair Stochastic Optimization (DFSO) based on the Wasserstein fairness measure. The DFSO aims to minimize distributional disparities among groups, quantified by the Wasserstein distance, while adhering to an acceptable level of inefficiency. Our analysis reveals that: (i) the Wasserstein fairness measure recovers the demographic parity fairness prevalent in binary classification literature; (ii) this measure can approximate the well-known Kolmogorov-Smirnov fairness measure with considerable accuracy; and (iii) despite DFSO's biconvex nature, the epigraph of the Wasserstein fairness measure is generally Mixed-Integer Convex Programming Representable (MICP-R). Additionally, we introduce two distinct lower bounds for the Wasserstein fairness measure: the Jensen bound, applicable to the general Wasserstein fairness measure, and the Gelbrich bound, specific to the type-2 Wasserstein fairness measure. We establish the exactness of the Gelbrich bound and quantify the theoretical difference between the Wasserstein fairness measure and the Gelbrich bound.

Bio: 

Dr. Weijun Xie is the Coca-Cola Foundation Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Xieobtained his Ph.D. in Operations Research at the Georgia Institute of Technology in 2017. His research interests are theory and applications of stochastic, discrete, and convex optimization. His works have received multiple awards, including the 2022 New Investigator Award from the Virginia Space Grant Consortium (NASA), the 2021 NSF CAREER Award, and the Winner of the 2020 INFORMS Young Researchers Paper Prize. He currently serves as Associate Editor of Operations Research, Mathematical Programming, and the Journal of Global Optimization.