Wenpin Tang


Research Interests

Stochastic Processes, Fintech, Machine Learning, Statistics

Wenpin Tang works at the intersection of stochastic analysis, machine learning and quantitative finance. His primary research areas are continuous-time stochastic processes and probabilistic ranking models. Continuous-time stochastic processes, arising as the limit of discrete algorithms and large particle systems, provide feasible analysis and unique insight into real-world problems of machine learning and finance. Ranking models serve as fundamental tools to understand various social phenomena such as elections and recommendation mechanism.

Tang’s current research interest is to improve the efficiency of machine learning algorithms using stochastic tools, and to develop robust AI methodology for the emerging fintech market. Examples include random graph modeling and queueing analysis of blockchain protocols, dynamic portfolio selection via repulsive point processes, and high-dimensional continuous optimization problems.

Tang was a postdoctoral researcher in the Department of IEOR at UC Berkeley from 2019-2020, and an assistant adjunct professor in the Department of Mathematics at UCLA from 2017-2019. He received his PhD in Statistics from UC Berkeley in 2017 and his engineering degree at Ecole Polytechnique in 2013. He received the Prize for Excellence in Financial Markets from Morgan Stanley in 2017.


  • Postdoctoral research, UC Berkeley, 2019-2020


  • Assistant Adjunct Professor, UCLA, 2017-2019


  • Prize for Excellence in Financial Markets, Morgan Stanley, 2017


  • Xin Guo, Fengmin Tang and Wenpin Tang, The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application, to appear in Proceedings of the 37th International Conference on Machine Learning (ICML 2020).
  • Jim Pitman and Wenpin Tang, Regenerative random permutations of integers, Annals of Probability (2019) vol.47, no.3, 1378-1416.
  • Wenpin Tang, Mallows ranking models: maximum likelihood estimate and regeneration, Proceedings of the 36th International Conference on Machine Learning (ICML 2019) PMLR 97, 6125-6134.
  • Wenpin Tang, Exponential ergodicity and convergence for generalized reflected Brownian motion, Queueing Systems: Theory and Applications (2019) vol.92, no.1, 83-101.
  • Wenpin Tang and Li-Cheng Tsai, Optimal surviving strategy for drifted Brownian motions with absorption, Annals of Probability (2018) vol.46, no.3, 1597-1650.