Industrial Engineering and Operations Research
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.
- 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.