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I am now working at ExxonMobil Upstream Research Company. Prior to join ExxonMobil, I worked at Microsoft.

I got my Ph.D and M.S degrees in Mechanical Engineering with minors in "Operations Research and Information Engineering" and "Theoretical and Applied Mechanics" from Cornell University, where I worked in the Materials Process Design and Control (MPDC) Laboratory, directed by Prof. Nicholas Zabaras. I got my Bachelor of Engineering degree in Automation, which is a research area in Electrical Engineering, from University of Science and Technology of China.

I work in the broad area of computational statistics and mechanics. My primary area of research can be described as the fusion of applied mathematics and parallel high performance computing concepts coupled with realistic physical modeling towards the analysis, control and design of complex systems. I am particularly interested in understanding how uncertainty propagates through and affects complex multiscale, multicomponent and multiphysics systems. Understanding the effects of permeability variations on the outputs of various multi-phase flow transport phenomena is one of the major application areas. Another important application area is to utilize machine learning algorithm coupled with stochastic modeling to generate probabilistic models from only limited experimental data. I am also interested in developing efficient algorithms for Bayesian Inference approach to solve inverse problem for various engineering applications.

Areas of interest: Computational mechanics, Computational statistics, Reservoir simulation, Stochastic modeling, Uncertainty quantification and propagation, Multiscale modeling, Stochastic reduced-order modeling, Control and optimization of complex systems, Inverse problem, Bayesian Inference, Markov Chain Monte Carlo, machine learning and large scale high performance parallel scientific computing.