Peter Carr Brooklyn Quant Experience (BQE) Lecture Series: Xin Zhang

Title 

Wasserstein Space of Stochastic Processes 

Abstract

The problem of optimal transport was first proposed by Gaspard Monge in 1781, and it defines the so-called Wasserstein distance between probability measures. Recently, it has become a fundamental tool in applied mathematics such as machine learning and image processing.

In this talk, we will construct a suitable adapted Wasserstein distance on the space of continuous time stochastic processes. We will show the continuity of various stochastic properties with respect to this metric, and its applications in stochastic control, mathematical finance, and model uncertainty problems. The goal is to understand the underlying structure of space of stochastic processes. This talk is based on the joint work with Daniel Bartl, Mathias Beiglböck, Gudmund Pammer, and Stefan Schrott. 

Bio

Dr. Zhang is now a postdoc at the University of Vienna, working in the group of Prof. Mathias Beiglböck since 2021. Before that, he received his Ph.D. in 2021 at the University of Michigan under the supervision of Prof. Erhan Bayraktar and earned his Bachelor's in 2016 at Fudan University. Dr. Zhang’s research focuses on stochastic control, optimal transport, and math finance.

This event will take place in person and online.

Meeting ID: 994 3085 6954
Password: PCBQE229