Zeyu Zheng

Assistant Professor

University of California

Berkeley, CA 94720

Office: 4125 Etcheverry Hall


Education Background

Ph.D. in Management Science and Engineering, Stanford University, Stanford, CA, 2018

• Advisor: Peter W. Glynn

Ph.D. Minor in Statistics, Stanford University, Stanford, CA, 2016

M.A. in Economics, Stanford University, Stanford, CA, 2016

B.S. in Mathematics, Peking University, Beijing, China, 2012

Research Interests

• Simulation

• Stochastic and statistical modeling

• Machine learning for decision making

• Financial technologies

Publications and Preprints

Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates, with Harsha Honnappa and Peter W. Glynn, 2018, submitted for publication.

Approximating Performance Measures for Slowly Changing Non-stationary Markov Chains, with Harsha Honnappa and Peter W. Glynn, 2018, submitted for publication.

Conflicted Immediacy Provision, with Yu An, 2018, submitted for publication.

Rates of Convergence and CLTs for Subcanonical Debiased MLMC, with Jose Blanchet and Peter W. Glynn, 2018, Monte Carlo and Quasi-Monte Carlo Methods, Springer Proceedings in Mathematics & Statistics, vol 241, pp 465-479.

Fitting Continuous Piecewise Linear Poisson Intensities via Maximum Likelihood and Least Squares, with Peter W. Glynn, Proceedings of the Winter Simulation Conference 2017.

A CLT for Infinitely Stratified Estimators, with Applications to Debiased MLMC, with Peter W. Glynn, ESAIM: Proceedings and Surveys (B. Bouchard, E. Gobet and B. Jourdain, Editors), vol 59, pp 104-114.

Extensions of the Regenerative Method to New Functionals, with Peter W. Glynn, Proceedings of the Winter Simulation Conference 2016, pp 289-301.


Working Papers

  • Poisson Autoregressive Models for Arrival Data (with Peter W. Glynn and Xiaowei Zhang)
  • Demand Prediction, Predictive Shipping, and Product Allocation for Large-scale E-commerce (with Xiaocheng Li, Yufeng Zheng, and Zhenpeng Zhou), Finalist, 2018 MSOM Data Driven Research Challenge
  • Efficient Real-time Arrivals Prediction (with Peter W. Glynn)
  • Data-driven Ranking and Selection with High Dimensional Covariates (with Xiaocheng Li)
  • Modeling and Testing Block Arrival Patterns
  • Testing the Poisson Assumption for Stationary and Non-stationary Data (with Harsha Honnappa and Peter W. Glynn)

Teaching

  • Spring 2019, IEOR 173, Introduction To Stochastic Processes







.