Ming Zhong

  • Assistant Professor of Applied Mathematics

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Education

Ph.D., University of Maryland, College Park, MD

M.S., California State Polytechnic University - Pomona, Pomona, CA

B.S., University of Oregon, Eugene, OR

Research Interests

Scientific Machine Learning, Inverse Problems, Probabilistic Numerics, Numerical
Methods, Modeling and Simulation.

Publications

  1. J. Park, C. Saltijeral, M. Zhong. Grassmanian Packings: Trust Region Stochastic Tuning for Matrix Inconherence, accepted to the 58th Allerton Conference, 2022.
  2. E. J. R. Coutinho, M. Dall’Aqua, L. McClenny, M. Zhong, U. Braga-Neto, and E. Gildin. Stabilized hyperbolic pde solver by adding adaptive localized artificial viscosity to physics-informed neural networks, 2022. Submitted.
  3. J. Feng, M. Maggioni, M. P. Martin, and M. Zhong. Learning interaction variables and kernels from observation of agent-based systems, Accepted to MTNS 2022.
  4. S. Foucart, E. Tadmor, and M. Zhong. On the sparsity of lasso minimizers in sparse data recovery, Accepted by Constructive Approximation 2022.
  5. B. Ganis, I. Yotov, and M. Zhong. A stochastic mortar mixed finite element method for flow in porous media with multiple rock types. SIAM J. Sci. Comp., 33(3):1439 – 1474, 2011.
  6. F. Lu, M. Zhong, S. Tang, and M. Maggioni. Nonparametric inference of interaction laws in systems of agents from trajectory data. PNAS, 116(29):14424 – 14433, July 2019.
  7. M. Maggioni, J. Miller, H. Qiu, and M. Zhong. Learning interaction kernels for agent systems on Riemannian manifolds. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 7290–7300. PMLR, 18–24 Jul 2021.
  8. M. Zhong. Hierarchical Reconstruction Method for Solving Ill-posed Linear Inverse Problems. PhD thesis, University of Maryland, College Park, MD, May 2016.
  9. M. Zhong, J. Miller, M. Maggioni. Data-driven discovery of emergent behaviors in collective dynamics, Physica D. 116 (29): 14424 – 14433. June 2020
  10. M. Zhong, J. Miller, and M. Maggioni. Machine learning for discovering effective interaction kernels between celestial bodies from ephemerides, Submitted 2021.