My previous research mainly focused on data-driven turbulence modeling by using Bayesian inference and machine learning techniques. When I was at Lawrence Berkeley National Laboratory, I also explored generative learning techniques (e.g. generative adversarial networks) to emulate and predict PDE-governed systems. My current research focuses on data-driven methods to improve the modeling of multi-scale dynamical systems and to quantify the model-form uncertainty.


In general, my research interests lie in an interdisciplinary area of computational physics, applied mathematics and statistics.



2014 - 2018

Virginia Tech, United States

Ph.D., Aerospace Engineering

2011 - 2014

Southeast University, China

M.S., Power Engineering

2007 - 2011

Southeast University, China

B.S., Thermal Energy and Power Engineering

01/2019 -

Computing and Mathematical Sciences, Caltech

Environmental Science and Engineering, Caltech

Postdoctoral Researcher

09/2018 - 12/2018

Institute for Pure and Applied Mathematics, UCLA

Visiting Scholar

05/2018 - 08/2018

Lawrence Berkeley National Laboratory

Summer Intern

06/2016 - 07/2016

Center for Turbulence Research, Stanford University

Visiting Graduate Student