### Introduction

**
I am on the job market for tenure-track Assistant Professor, Research Scientist, or Research Staff.
**

I am a postdoctoral researcher in the Department of Applied Mathematics & Statistics at Johns Hopkins University. I am working with Mauro Maggioni on various machine learning projects to study collective dynamics used to investigate a special phenomenon called self-organization, where global orders would emerge from initial chaos via mere local interactions.

Before my postdoc appointment at JHU, I was a doctoral student in the Applied Mathematics & Statistics, and Scientific Computing (AMSC) program at University of Maryland. My thesis adviser was Eitan Tadmor (Wikipedia Page).

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### Research Interests

I work on the problems in the mathematical and computational foundations of Data Science, motivated by the need of making scientific discoveries from observations. In particular, I develop and analyze algorithms to build predictive and interpretable models to explain the observation data. The analysis of these algorithms requires ideas from Inverse Problems, Approximation Theory, Dynamical Systems, Numerical ODE/PDE, Probability and Statistics. Oftentimes, I am dealing with data sets with enormous size, hence the algorithms have to be scalable and efficient. I employ techniques from multi-scale analysis, dimension reduction, domain decomposition, and parallel computing, to reduce the computing time.

Right now, I am considering the following problems:

- Discovery of governing structures of ODE/PDE driven dynamics
- Ill-posed inverse problems for data recvoery
- Machine learning for numerical ODE/PDE and data recovery problems
- Computational optimal transport distance/plan for high-dimensional objects
- Geometric Numerical Integration, including symplectic integration, to preserve geometric properties of the dynamics.
- Computation of geodesics and geodesic distance on Riemannian manfiolds.
- New central schemes for conservative systems (see CentPack @ CSCAMM)

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### Contact Information

- Office: Krieger 411
- Email: mzhong5 ''at'' jhu ''dot'' edu

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### Select Publications

- M. Maggioni, J. Miller, H. Qiu, M. Zhong. Learning interaction kernels for agent systems on Riemannian manifolds, submitted (arXiv link available upon request).
- J. Miller, S. Tang, M. Zhong and M. Maggioni. Learning theory for inferring interaction kernels in second-order interacting agent systems, submitted (arXiv page).
- E. Tadmor, M. Zhong. Sparse Recovery of Noisy Data Using the LASSO Method, submitted (arXiv page).
- M. Zhong, J. Miller, M. Maggioni. Data-driven Discovery of Emergent Behaviors in Collective Dynamics (arXiv page), Physica D: nonlinear phenomenon, 411, 132542, October 2020.
- F. Lu, M. Zhong, S. Tang, M. Maggioni. Nonparametric inference of interaction laws in systems of agents from trajectory data (arXiv page), PNAS, 116 (3), 14424 - 14433, June 2019.

A complete list of publications can be found here.

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