### Introduction

I obtained my doctoral degree in applied mathematics from the Applied Mathematics & Statistics, and Scientific Computing (AMSC) program (ranked 13th in the U.S.) in the Department of Mathematics at the University of Maryland in May of 2016, under the guidance of the Distinguished University Professor, Dr. Eitan Tadmor (his wikipedia page). My doctoral thesis analyzes the usage of a Hierarchical Reconstruction method on solving a series of ill-posed problems:

- Sparse recovery from noisy observation.
- Linear regression from noisy responses.
- De-convolution of discrete Helmholtz differential filter from noisy data.

- A Multiscale Solver for Optimal Transport Plan, and Applications of Wasserstain Metric.
- Inferring Interaction Rules for Self-Organzied Dynamics.

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### Research Interests

I'm interested in a wide range of topics related to Data Science, Inverse Problems, Machine Learning, Modeling and Simulation:

- Learning interaction laws from observing agent based dynamics; modeling of agent based dynamics
- Linear and non-linear ill-posed Inverse Problems.
- Hierarchical reconstruction method for data recovery.
- Uncertainty quantificatoin with Stochastic PDEs.
- Parameter estimation for PDEs.
- Centeral schemes for conservation laws.
- Scientific computing with MPI, openMP, GPU.
- Learning algorithms implementation with MATLAB and Python.

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A copy of my detailed CV can be found here.

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