ORIE Colloquium: Wenpin Tang (UC Berkeley) - Discrete and continuous ranking models

Description

In this talk, I will discuss two different 'ranking' models: rank-dependent diffusions and Mallows' ranking models. In the first part, I will discuss the rank-dependent diffusions. I will focus on two continuous optimization problems: Up the River model, and N-player games with fuel constraints. These problems require treating carefully the corresponding PDEs, and using the idea of rank-dependent diffusions. In the second part, I will focus on the Mallows' ranking, and various generalizations. In particular, I will explain how the idea of regeneration is used for the data-driven process. I will also introduce a general model, called regenerative permutation, and discussed the statistical properties, related algorithms and applications. If time permits, I will discuss recent progress on the random walk derived from random permutations, which is motivated by applications in systems biology.

Bio:
Wenpin Tang is a Postdoctoral Researcher in IEOR, UC Berkeley, and before that an assistant adjunct professor in mathematics at UCLA. He earned his Ph.D. degree in statistics from UC Berkeley, advised by Jim Pitman. He specializes in stochastic processes and learning theory, and has broad interests in financial engineering, statistics, mathematical biology and queuing systems. Recently he is interested in large network analysis motivated by blockchain development. He also got several honors including Morgan Stanley’s Annual Prize for Excellence.