Areas of Interest
Deep Learning Theory for Partial Differential Equations, Approximation and Statistical Learning Theory of Neural Networks, Mathematical Modeling and Simulation in Materials Science and Biology.
Research
My research develops the mathematical theory and algorithms of deep learning for solving complex partial differential equations, including approximation and generalization analysis of neural networks and new methods such as operator learning and homotopy-based training. I also build mathematical models for problems in materials science and biology, often using partial differential equations and stochastic partial differential equations as the underlying framework.
Office Hours
Courses
Fall 2026
| Course |
Section |
Title |
Schedule |
Room |
| Math 447 |
01 |
Probability Theory |
MWF 8:30–9:30
|
OH-G102
|
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