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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).

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:

Contact Information

Select Publications

A complete list of publications can be found here.