Recent PhD Graduates

Our PhD graduates explore a variety of research topics while at Wash U and find success in diverse academic and professional pursuits after graduation.

The following is a chronological list of PhD graduates from the Washington University in St. Louis Statistics and Data Science Department, including graduates from when the SDS department was combined with the Math Department under the name "Department of Mathematics and Statistics."

Note: there are some instances of advising faculty members from other disciplines because of the interdisciplinary interests of our department.

  • Zhetao Chen, SP24 (Soumendra Lahari and Muriah Wheelock), Assessing reproducibility of Brain-behavior associations using bootstrap aggregation methods

  • Jiaqi Li, SP24 (Likai Chen and Todd Kuffner), Inference for Time Series in Change Points and Statistical Learning, William H. Kruskal Instructor at the University of Chicago
     
  • Chang Liu, FL23 (Jose Figueroa-Lopez), Market Making with Latency, TBA
     
  • Yanjie Zhong, SP23 (Soumendra Lahiri), Online Bootstrap Inference with Nonconvex Stochastic Gradient Descent Estimator,  R&D Engineer at DyteDance in San Jose, CA.

  • Dhrubajyoti Ghosh, SU22 (Soumendra Lahiri), Contribution to Data Science: Time Series, Uncertainty Quantification and Applications, Postdoctoral Associate, Duke University, Department of Biostatistics, Durham, N.C.

  • Yuchen Han, SU22 (Jose Figueroa-Lopez), Truncated Realized Variations of Lévy Models: Optimality, Debiasing, and Implementation Approaches, Research Scientist at Meta Platforms in New York, NY.

  • Bei Wu, SU22 (Jose Figueroa-Lopez), Kernel Estimation of Spot Volatility and Its Application in Volatility Functional Estimation, Data Scientist, Amazon, Cupertino, CA.

  • Cezareo Rodriguez, SU22 (Nan Lin), Dealing with Dimensionality: Problems and Techniques in High-Dimensional Statistics, Algorithm Engineer, SimpleRose, Inc., St. Louis, MO.

  • Jiayi Fu, Summer 2021 (J. Ding), Smooth ICA Model and Multi-Level ICA Model under Assumptions of Time Pattern, Machine Learning Engineer, ByteDance, Mountian View, California.

  • Chuyi Yu, Summer 2021 (J. Figueroa-Lopez), Market Making in a Limit Order Book: Classical optimal control and Reinforcement Learning Approaches, Associate Quantitative Analysist, Barclays Service Corporation, New York, N.Y.

  • Qi Wang, 2020 (J. Figueroa-Lopez and T. Kuffner), Bayesian Posterior Inference and LAN for Lévy Models Under High-frequency Data, Quantitative Analyst, Barclays, New York, N.Y.
     
  • Qiyiwen Zhang 2020 (T. Kuffner), Bayesian variable selection and post-selection inference, Postdoctoral Researcher, Pereleman School of Medicine/University of Pennsylvania, Pennsylvania, PA.
     
  • Guanshengrui Hao, 2019 (N. Lin), Topics in Complex and Large-scale Data Analysis, Data Scientist, Google, San Francisco, CA.
     
  • Wei Wang, 2019 (N. Lin), Three Essays on Complex Dependent Data, Senior Scientist, Merck & Co., Inc. in Rahway, N.J.
     
  • Xiaoyu Dai, 2018 (N. Lin), Large-scale Multiple Hypothesis Testing with Complex Data Structure, Quantitative Analyst, Google, Inc., Mountain View, CA.
     
  • Tian Wang, 2018 (J. Ding), Joint Model for Phase and Amplitude Variation in Functional Data, Postdoc Research Scientist, Columbia University, New York, NY.
     
  • Liqun Yu, 2018 (N. Lin), Distributed Quantile Regression Analysis, Data Scientist, Apple Inc., Cupertino, CA.
     
  • Chao Chang, 2015, (Nan Lin), Nonparametric Bayesian Quantile Regression, Data Analyst, Google, San Francisco, CA.