معرفی
Ning Patricia Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and Institute for Quantum Science and Engineering. She holds a B.S. from Shandong University, M.S. from USC, and Ph.D. from UCSB. Her research focuses on stochastic processes, Markov chains, Bayesian inference, networks, machine learning, and quantum computing. She has served as an Associate Editor for Bayesian Analysis, Journal of Computational and Graphical Statistics, and Statistics and Computing. Recent contributions include advancements in temporal interference stimulation modeling and applications of sequential Monte Carlo methods in machine learning.
Her work bridges theoretical stochastic analysis with practical applications in epidemiology and finance. Notable publications include studies on MCMC optimal scaling and doubly reflected backward SDEs. She actively participates in academic events such as the Stochastic Numerics and Statistical Learning Workshop 2025 and the TAMIDS SciML Workshop. As an NSF panelist, she contributes to funding evaluations in mathematical sciences.
Ning’s interdisciplinary approach integrates computational methods with real-world challenges, emphasizing scalable Bayesian inference and quantum algorithms. She has advised on multiple research initiatives and maintains a lab focused on stochastic processes and data science applications.
