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About Me:

I am an Assistant Professor in the Dept. of Statistics with affiliation to Institute of Data Science and Institute for Quantum Science and Engineering at Texas A&M University. I am the co-founder of the Stochastic Processes Seminar in the Dept. of Mathematics. 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. I have been serving as an Associate Editor for the following journals:

• Bayesian Analysis, since Jan. 2025.

• Journal of Computational and Graphical Statistics, since Jan. 2024.

• Statistics and Computing, since Jul. 2023.

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, machine learning, and quantum computing. 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, asymptotics, online learning, reinforcement learning, and their quantum counterparts. 

Recent News:

• 06/2025 I presented an invited 50-minute talk titled "Metropolis-adjusted Subdifferential Langevin Algorithm" at the two-week scientific program "Bayesian Learning of Very High-Dimensional Physical Process Models" hosted by the MATRIX Institute, University of Melbourne, Australia.

• 06/2025 I delivered a talk at the invited session of the 4th Bayes Comp 2025, National University of Singapore, titled "Scalable Bayesian Inference for Large Language Model Analysis".

• 06/2025 My paper An Assessment of Ensemble Kalman Filter and Azouani-Olson-Titi Algorithms for Continuous Data Assimilation: A Comparative Study is accepted to Communications in Computational Physics. 

• 06/2025 My paper Detecting Structural Shifts and Estimating Change-Points in Interval-Based Time Series is accepted to Statistics and Computing.

• 05/2025 My paper Temporal Interference Stimulation: Mechanisms, Optimization, Validation, and Clinical Prospects—A Comprehensive Review is accepted to WIREs Computational Statistics.

• 05/2025 I delivered an invited 45 mins talk at the Stochastic Numerics and Statistical Learning Workshop 2025, KAUST, Saudi Arabia, titled "Metropolis-adjusted Subdifferential Langevin Algorithm".

• 05/2025 I delivered an invited 45 mins talk at the International Seminar on Monte Carlo Methods, titled "Convergence of Dirichlet forms for MCMC optimal scaling with dependent target distributions on large graphs"

• 05/2025 I delivered an invited 45 mins talk at the Learning Seminar in Fundamentals of Data Analysis, the Graduate Center, City University of New York, titled "Convergence of Dirichlet forms for MCMC optimal scaling with dependent target distributions on large graphs"

• 04/2025 I have a new manuscript available Advancements in temporal interference stimulation: A comprehensive review of computational innovations and clinical applications

• 04/2025 I delivered an invited talk at Spring 2025 Scientific Machine Learning Workshop, titled "Sequential Monte Carlo: Applications in Machine Learning and Large Language Models"

• 03/2025 I delivered a 45 mins talk at the StatCafe seminar, titled "Scalable Bayesian Inference for Large Language Model Analysis"

• 02/2025 My paper Doubly Reflected Backward SDEs Driven by G-Brownian Motions and Fully Nonlinear PDEs with Double Obstacles is accepted to Stochastics and Partial Differential Equations: Analysis and Computations.

• 02/2025 My new manuscript is available at arXiv: Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants

• 12/2024 With a great pleasure, I am going to serve as an Associate Editor for Bayesian Analysis from Jan. 2025.

• 12/2024 I delivered a talk as an invited speaker at the Analysis and Probability Seminar organized by the Department of Mathematics at Iowa State University.

• 11/24 My paper Convergence of Dirichlet Forms for MCMC Optimal Scaling with General Target Distributions on Large Graphs is accepted to Annals of Applied Probability.

• 11/24 My paper  Modeling the Impacts of Governmental and Human Responses on COVID-19 Spread Using Statistical Machine Learning is accepted to International Journal of Digital Earth.

• 11/2024 With a great pleasure, I will be serving as an NSF panelist for the Division of Mathematical Sciences (DMS).

• 10/2024 My paper Understanding Human-COVID-19 Dynamics using Geospatial Big Data: A Systematic Literature Review is accepted to Annals of GIS.

• 10/2024  I will serve as an Invited Session Organizer of JSM 2025, which will be held in Nashville, Tennessee, from August 2-7, 2025 (https://ww2.amstat.org/meetings/jsm/2025/).

• 10/2024 I have a new manuscript available Detecting Structural Shifts and Estimating Change-Points in Interval-Based Time Series

• 10/2024 I gave a talk as an invited speaker at the 7th Annual Meeting of the SIAM TX-LA Section, Baylor University.