Hongkai Zhao, Ruth F. DeVarney Distinguished Professor

My research interest is in computational and applied mathematics that includes modeling, analysis and developing numerical methods for problems arising from science and engineering. More specifically, I have worked on:

• numerical analysis and scientific computing;
• multi-scale, multi-physics and multi-phase problems in wave propagation, fluids, and materials;
• inverse problems related to medical and seismic imaging;
• computer vision and image processing.

Contact Info:

Office Location:
Office Phone:	(919) 660-2800
Email Address:

Teaching (Spring 2024):

• MATH 563.01, APPLIED COMPUTATIONAL ANALYSIS Synopsis

  Physics 227, WF 03:05 PM-04:20 PM

Office Hours:

Wednesday 3-5pm

Education:

Ph.D.

University of California, Los Angeles

1996

Recent Publications   (More Publications)

1. Li, S; Zhang, C; Zhang, Z; Zhao, H, A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems, Statistics and Computing, vol. 33 no. 4 (August, 2023) [doi]  [abs]
2. He, Y; Zhao, H; Zhong, Y, How Much Can One Learn a Partial Differential Equation from Its Solution?, Foundations of Computational Mathematics (January, 2023) [doi]  [abs]
3. Zhang, S; Lu, J; Zhao, H, On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 41452-41487  [abs]
4. Zhao, H; Zhong, Y, How much can one learn from a single solution of a PDE?, Pure and Applied Functional Analysis, vol. 8 no. 2 (2023), pp. 751-773
5. Zhao, H; Zhong, Y, Quantitative PAT with simplified P N approximation, Inverse Problems, vol. 37 no. 5 (May, 2021) [doi]  [abs]

Recent Grant Support

• Learning Partial Differential Equation (PDE) and Beyond, National Science Foundation, 2023/07-2026/06.      
• RTG: Training Tomorrow's Workforce in Analysis and Applications, National Science Foundation, 2021/07-2026/06.      
• Computational Forward and Inverse Radiative Transfer, National Science Foundation, 2020/07-2024/06.