Multistage Distributionally Robust Optimization with Nested Distance
In this talk, I will discuss multistage distributionally robust optimization, where the uncertainty set is a ball of distributions defined through the nested distance (Pflug and Pichler 2012). This choice of uncertainty set, as opposed to alternatives like the Wasserstein distance between stochastic processes, takes into account information evolution, making it hedge against a plausible family of data processes. First, I will present a recursive reformulation to evaluate the worst-case risk of any given random sequence and explore the intricacies of time consistency in dynamic risk measures. Next, I will present dynamic programming reformulations for finding the optimal policy in the linear and stagewise-independent setting with objective / right-hand side uncertainty.
Rui Gao is an Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin. He received a Ph.D. in Operations Research from Georgia Institute of Technology in 2018, and a B.Sc. in Mathematics and Applied Mathematics from Xi'an Jiaotong University in 2013.
Rui's main research studies data-driven decision-making under uncertainty and prescriptive data analytics. His research has been recognized with several INFORMS paper competition awards, including Winner in Junior Faculty Interest Group Paper Competition (2020), Winner in Data Mining Best Paper Award (2017), Runner-up in Computing Society Student Paper Award (2017), and Finalist in George Nicholson Student Paper Competition (2016). He currently serves as an Associate Editor for Mathematical Programming.
B.Sc. in Mathematics and Applied Mathematics (Honors Program)
Special Class for the Gifted Young (2007-2009)
SAS Institute Inc., Advanced Analytics R&D, Raleigh-Durham, NC, 2016
Revenue Management and Price Optimization Summer Fellow