Welcome to my homepage

I am an Assistant Professor at the Department of Mathematics, University of Wisconsin-Madison. I am also a faculty affiliate of the Institute for Foundations of Data Science (IFDS), a multi-University TRIPODS Phase II Initiative.

I received my PhD degree from the Courant Institute of Mathematical Sciences (CIMS) and the Center of Atmosphere and Ocean Science (CAOS), New York University (NYU) in May 2016. After that, I was a postdoc research associate at CIMS, NYU from June 2016 to May 2018. My PhD advisor and postdoc mentor were both Dr. Andrew Majda. My undergraduate major was Mechanical Engineering, Fudan University in Shanghai and I received my Master's degree at the School of Mathematical Sciences Fudan University, working with Dr. Jin Cheng, during which time I also visited the Department of Scientific Computing at Florida State University for one year, working with Dr. Max Gunzburger and Dr. Xiaoming Wang.

My research interests lie in the contemporary applied mathematics: modeling complex systems, stochastic methods, numerical algorithms, geophysics, machine learning techniques and general data science. Problems with large dimensional, turbulence and partial information are particularly what I am concerned with. Mathematical and physical problems in uncertainty quantification (UQ), data assimilation, information theory, scientific machine learning, applied stochastic analysis, inverse problems, high-dimensional data analysis and effective prediction all belong to my research topics. I am also devoted to proposing efficient and statistically accurate algorithms to ameliorate the curse of dimensionality for large-dimensional complex dynamical systems with strong non-Gaussian features. In addition, I'm active in developing both dynamical and stochastic models and use these models to predict real-world phenomena related to atmosphere ocean science, climate and other complex systems such as the Madden-Julian Oscillation (MJO), the monsoon, the El Nino Southern Oscillation (ENSO) and the sea ice based on real observational data. My recent work also involves the development of new UQ and stochastic methods to material science. The mathematical and computational tools developed in my work can be of great interest to diverse fields such as atmosphere ocean science, climate, material science, neuroscience, excitable media, physics and engineering.

I have teaching experience for different courses ranging from undergraduate to graduate levels, including Calculus, Numerical Methods, Uncertainty Quantification, Data Assimilation and Stochastic Computational Methods. My lecture notes on the advanced course: "Topics in Applied Math: Uncertainty Quantification, Data Assimilation and Prediction" can be found through the "course" link above (or here). My outreach work for writing public articles, giving broader lectures for strenghening undergrad education and mentoring undergrads for summer research can be found here.

My new book "Stochastic Methods for Modeling and Predicting Complex Dynamical Systems --- Uncertainty Quantification, State Estimation, and Reduced-Order Models" published by Springer as part of the book series: Synthesis Lectures on Mathematics & Statistics (SLMS) has been scheduled to appear on early 2023. The link to the book on the Springer website is here.

I am looking for new Ph.D. students highly motivated for interdisciplinary research between applied math and (1) geophysics and climate science and (2) engineering and material science. If you have already been in our math Ph.D. program (or have been admitted) and are interested in my work, please feel free to contact me. I have research assistant positions open (so you will be partially exempt from the regular teaching assistant duties).

(Last updated 08/07/2023)