About Me:

I am an Assistant Professor in the Dept. of Statistics with affiliation to Institute of Data Science at Texas A&M University. I hold a B.S. of Math in the China Math Base at Shandong University and a M.S. of Math in Dept. of Math at University of Southern California. I received my PhD in the Dept. of Statistics and Applied Probability at UCSB, of Math Subject Classification: 60 – Probability Theory and Stochastic Processes. I hold a one year position as Postdoctoral Research Associate in the Dept. of Applied Math at the Univ. of Washington, Seattle and a three year position as Postdoctoral Research Fellow in the Dept. of Statistics at the University of Michigan, Ann Arbor. (Picture at IMS annual meeting in London, June 2022)

Prior to my PhD study, I worked as software developer / database architect in industry in Los Angeles full-time for about 4 years. I specialize at massive data analysis, parallel computing, and user-friendly platform development with multiple languages: R (advanced R with S4 classes and methods), C, C++, Python, Java, SQL/Transaction SQL, Matlab, SAS (advanced programmer since 2012), etc. I am good at operating systems: Windows, Mac, and Linux, as well as systems interacting manipulations and cloud computing.

My research interest in general is stochastic processes, Markov chains, time series, networks, and machine learning. Specifically, my research interests and expertise, include but are not limited to, the following topics: modern stochastic processes/Markov chains/time series analysis, networks/combinatorics/graphical models, high dimension, stochastic algorithm, Monte Carlo methods, hidden Markov/non-Markov models, and asymptotics. The main application areas of my research are epidemiology and finance.

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