Learning governing dynamics
Recovering low-dimensional laws from high-dimensional observations of interacting and stochastic systems.
University of Houston
Ming Zhong
Assistant Professor · Department of Mathematics
I develop mathematical and computational methods that learn scientific models from observational data.
About
I am an Assistant Professor in the Department of Mathematics at the University of Houston. My research lies at the intersection of inverse problems, scientific machine learning, learning dynamical systems, and numerical analysis.
Before joining the University of Houston in 2024, I was an Assistant Professor at Illinois Institute of Technology, an Assistant Research Scientist at the Texas A&M Institute of Data Science, and a postdoctoral researcher at Johns Hopkins University. I received my Ph.D. in Applied Mathematics from the University of Maryland.
I am a co-principal investigator on the NSF project A Bayesian Paradigm for Physics-Informed Machine Learning and received a Ralph E. Powe Junior Faculty Enhancement Award for data-driven modeling of emergent behavior in social science.
Research at a glance
Partially observed systems
Identifying hidden states and parameters when scientific observations are incomplete or indirect.
Computational foundations
Building stable, interpretable, and efficient numerical methods for scientific machine learning.
Contact
- University email
- mzhong4@uh.edu
- Office
- PGH 616
- Department
- Department of Mathematics
- Address
- University of Houston
Houston, TX 77204