Dr. Wenpin Tang

Biography

Wenpin Tang works at the intersection of stochastic analysis, machine learning, and quantitative finance. His primary research areas include continuous-time stochastic processes and probabilistic ranking models. Continuous-time stochastic processes, which arise as a limit of discrete algorithms in large particle systems, provide feasible analysis and unique insights into real-world problems in machine learning and finance. Ranking models serve as fundamental tools to understand social phenomena, such as elections and recommendation mechanisms. Tang’s current research interest is to improve the efficiency of machine learning algorithms using stochastic tools and to develop robust AI methodologies for the emerging fintech market. Examples of his work include random graph modeling, queueing analysis of blockchain protocols, dynamic portfolio selection, and high-dimensional continuous optimization problems. Previously, Tang worked as a postdoctoral researcher in the Department of Industrial Engineering and Operations Research at UC Berkeley from 2019 to 2020, and as an assistant adjunct professor in the Department of Mathematics at UCLA from 2017 to 2019. He received his PhD in Statistics from UC Berkeley in 2017 and his engineering degree from Ecole Polytechnique in 2013.

Research Interests

Experience

Awards