Math @ Duke
|
Hongkai Zhao, Ruth F. DeVarney Distinguished Professor and Chair
![Hongkai Zhao](https://fds.duke.edu/photos/fac/u22490.jpg) 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.
My Google Scholar Citations- Contact Info:
Office Location: | | Office Phone: | (919) 660-2800 | Email Address: | ![](https://fds.duke.edu/db/aas/math/faculty/hz220/email.png) ![send me a message](https://fds.duke.edu/photos/fac/New.gif) | - Office Hours:
- Wednesday 3-5pm
- Education:
Ph.D. | University of California, Los Angeles | 1996 |
- Recent Publications
(More Publications)
- 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]
- 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]
- 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]
- 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
- 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.
|
|
dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
| |
Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320
|
|