### 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 Inverse Problems, Modeling and Simulation, Numerical Analysis, Computational Statistical Inference, Machine/Statistical Learning, Data Science, etc.:

- Inferring Interaction from Observing Self-Organzied Dynamics
- Computational Methods for Statistical Inference
- Multi-scale solver for linear Inverse Problems
- De-convolusion solver for LES
- Uncertainty Quantification for Stochastic Darcy's Equation

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A copy of my detailed CV can be found here.

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