Zeyu Zheng
Zeyu Zheng
Assistant Professor
Assistant Professor
University of California, Berkeley
University of California, Berkeley
Office: 4125 Etcheverry Hall
Office: 4125 Etcheverry Hall
Education Background
Education Background
Ph.D. in Management Science and Engineering, Stanford University, 2018
Ph.D. in Management Science and Engineering, Stanford University, 2018
M.A. in Economics, Stanford University, 2016
M.A. in Economics, Stanford University, 2016
B.S. in Mathematics, Peking University, 2012
B.S. in Mathematics, Peking University, 2012
Research
Research
• Stochastic simulation
• Stochastic simulation
• Non-stationary stochastic modeling
• Non-stationary stochastic modeling
Publication
Publication
• Dynamic Pricing with External Information and Inventory Constraint, with Xiaocheng Li, accepted by Management Science, 2023.
• Dynamic Pricing with External Information and Inventory Constraint, with Xiaocheng Li, accepted by Management Science, 2023.
• Gradient-based Simulation Optimization Algorithms via Multi-Resolution System Approximations, with Jingxu Xu, accepted by INFORMS Journal on Computing, 2023.
• Gradient-based Simulation Optimization Algorithms via Multi-Resolution System Approximations, with Jingxu Xu, accepted by INFORMS Journal on Computing, 2023.
• Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training, with Tingyu Zhu and Haoyu Liu, accepted by ACM Transactions on Modeling and Computer Simulation (TOMACS), 2023.
• Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training, with Tingyu Zhu and Haoyu Liu, accepted by ACM Transactions on Modeling and Computer Simulation (TOMACS), 2023.
• Combining Numerical Linear Algebra with Simulation to Compute Stationary Distributions, with Alex Infanger and Peter W. Glynn, Proceedings of the Winter Simulation Conference, 2022.
• Combining Numerical Linear Algebra with Simulation to Compute Stationary Distributions, with Alex Infanger and Peter W. Glynn, Proceedings of the Winter Simulation Conference, 2022.
• A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Multi-armed Bandits, with Feng Zhu and David Simchi-Levi, Conference on Neural Information Processing Systems (NeurIPS), 2022.
• A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Multi-armed Bandits, with Feng Zhu and David Simchi-Levi, Conference on Neural Information Processing Systems (NeurIPS), 2022.
• Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization, with Tianyi Lin and Michael I. Jordan, Conference on Neural Information Processing Systems (NeurIPS), 2022.
• Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization, with Tianyi Lin and Michael I. Jordan, Conference on Neural Information Processing Systems (NeurIPS), 2022.
• Bounding Stationary Expectations for Queues and Storage Processes Fed by Stationary Input Sequences, with Peter W. Glynn, Queueing Models and Service Management, 2022.
• Bounding Stationary Expectations for Queues and Storage Processes Fed by Stationary Input Sequences, with Peter W. Glynn, Queueing Models and Service Management, 2022.
• Non-stationary A/B Tests, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), research track, 2022.
• Non-stationary A/B Tests, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), research track, 2022.
• Inference on the Best Policies with Many Covariates, with Waverly Wei, Yuqing Zhou and Jingshen Wang, accepted by Journal of Econometrics, 2022.
• Inference on the Best Policies with Many Covariates, with Waverly Wei, Yuqing Zhou and Jingshen Wang, accepted by Journal of Econometrics, 2022.
• Gradient-based Algorithms for Convex Discrete Optimization via Simulation, with Haixiang Zhang and Javad Lavaei, accepted by Operations Research, 2022.
• Gradient-based Algorithms for Convex Discrete Optimization via Simulation, with Haixiang Zhang and Javad Lavaei, accepted by Operations Research, 2022.
• Stochastic L-Convex Function Minimization, with Haixiang Zhang and Javad Lavaei, Conference on Neural Information Processing Systems (NeurIPS), 2021.
• Stochastic L-Convex Function Minimization, with Haixiang Zhang and Javad Lavaei, Conference on Neural Information Processing Systems (NeurIPS), 2021.
• Uncertainty Quantification and Prediction for Mechanical Properties of Graphene Aerogels via Gaussian Process Metamodels, with Bowen Zheng and Grace X. Gu, Nano Futures, 2021.
• Uncertainty Quantification and Prediction for Mechanical Properties of Graphene Aerogels via Gaussian Process Metamodels, with Bowen Zheng and Grace X. Gu, Nano Futures, 2021.
• Scalable Graphene Defect Prediction using Transferable Learning, with Bowen Zheng and Grace X. Gu, Nanomaterials, 2021.
• Scalable Graphene Defect Prediction using Transferable Learning, with Bowen Zheng and Grace X. Gu, Nanomaterials, 2021.
• Dynamic Planning and Learning under Recovering Rewards, with Feng Zhu and David Simchi-Levi, International Conference on Machine Learning (ICML) 2021.
• Dynamic Planning and Learning under Recovering Rewards, with Feng Zhu and David Simchi-Levi, International Conference on Machine Learning (ICML) 2021.
• Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training, with Tingyu Zhu, Proceedings of the Winter Simulation Conference (WSC) 2021.
• Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training, with Tingyu Zhu, Proceedings of the Winter Simulation Conference (WSC) 2021.
• Efficient Computation for Stratified Splitting, with Peter W. Glynn, Proceedings of the Winter Simulation Conference (WSC) 2021.
• Efficient Computation for Stratified Splitting, with Peter W. Glynn, Proceedings of the Winter Simulation Conference (WSC) 2021.
• On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification, with Tianyi Lin, Elynn Y. Chen, Marco Cuturi, and Michael I. Jordan, International Conference on Artificial Intelligence and Statistics (AISTATS) 2021.
• On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification, with Tianyi Lin, Elynn Y. Chen, Marco Cuturi, and Michael I. Jordan, International Conference on Artificial Intelligence and Statistics (AISTATS) 2021.
• When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment, with Feng Zhu, International Conference on Machine Learning (ICML) 2020.
• When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment, with Feng Zhu, International Conference on Machine Learning (ICML) 2020.
• Joint Resource Allocation for Input Data Collection and Simulation, with Jingxu Xu and Peter W. Glynn, Proceedings of the Winter Simulation Conference (WSC) 2020.
• Joint Resource Allocation for Input Data Collection and Simulation, with Jingxu Xu and Peter W. Glynn, Proceedings of the Winter Simulation Conference (WSC) 2020.
• Simulating Nonstationary Spatio-Temporal Poisson Processes using Inversion Method, with Haoting Zhang, Proceedings of the Winter Simulation Conference (WSC) 2020.
• Simulating Nonstationary Spatio-Temporal Poisson Processes using Inversion Method, with Haoting Zhang, Proceedings of the Winter Simulation Conference (WSC) 2020.
• Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates, with Harsha Honnappa and Peter W. Glynn, 2020, Operations Research.
• Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates, with Harsha Honnappa and Peter W. Glynn, 2020, Operations Research.
• Method of Moments Estimation for Lévy-driven Ornstein-Uhlenbeck Stochastic Volatility Models, with Xiangyu Yang, Yanfang Wu, Jian-Qiang Hu, 2020, Probability in the Engineering and Informational Sciences.
• Method of Moments Estimation for Lévy-driven Ornstein-Uhlenbeck Stochastic Volatility Models, with Xiangyu Yang, Yanfang Wu, Jian-Qiang Hu, 2020, Probability in the Engineering and Informational Sciences.
• Estimation and Inference for Non-stationary Arrival Models, with Peter W. Glynn, 2019, Proceedings of the Winter Simulation Conference.
• Estimation and Inference for Non-stationary Arrival Models, with Peter W. Glynn, 2019, Proceedings of the Winter Simulation Conference.
• 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.
• 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.
• Data-driven Ranking and Selection with High Dimensional Covariates and General Dependence, with Xiaocheng Li and Xiaowei Zhang, 2018, Proceedings of the Winter Simulation Conference.
• Data-driven Ranking and Selection with High Dimensional Covariates and General Dependence, with Xiaocheng Li and Xiaowei Zhang, 2018, Proceedings of the Winter Simulation Conference.
• Fitting Continuous Piecewise Linear Poisson Intensities via Maximum Likelihood and Least Squares, with Peter W. Glynn, 2017, Proceedings of the Winter Simulation Conference.
• Fitting Continuous Piecewise Linear Poisson Intensities via Maximum Likelihood and Least Squares, with Peter W. Glynn, 2017, Proceedings of the Winter Simulation Conference.
• 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.
• 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, 2016, Proceedings of the Winter Simulation Conference.
• Extensions of the Regenerative Method to New Functionals, with Peter W. Glynn, 2016, Proceedings of the Winter Simulation Conference.
Working Paper
Working Paper
• Best Arm Identification with Fairness Constraints on Subpopulations, with Yuhang Wu and Tingyu Zhu.
• Best Arm Identification with Fairness Constraints on Subpopulations, with Yuhang Wu and Tingyu Zhu.
• Adaptive A/B Tests and Simultaneous Treatment Parameter Optimization, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang.
• Adaptive A/B Tests and Simultaneous Treatment Parameter Optimization, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang.
• A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Stochastic Bandits, with Feng Zhu and David Simchi-Levi; preliminary version appeared in NeurIPS 2022.
• A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Stochastic Bandits, with Feng Zhu and David Simchi-Levi; preliminary version appeared in NeurIPS 2022.
• Non-stationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang; preliminary version appeared in KDD 2022.
• Non-stationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang; preliminary version appeared in KDD 2022.
• Online Matching with Reusable Network Resources and Decaying Rewards: New Framework and Analysis, with Feng Zhu and David Simchi-Levi.
• Online Matching with Reusable Network Resources and Decaying Rewards: New Framework and Analysis, with Feng Zhu and David Simchi-Levi.
• Stochastic Localization Methods Algorithms for Convex Discrete Optimization via Simulation, with Haixiang Zhang and Javad Lavaei.
• Stochastic Localization Methods Algorithms for Convex Discrete Optimization via Simulation, with Haixiang Zhang and Javad Lavaei.
• Offline Planning and Online Learning under Recovering Rewards, with Feng Zhu and David Simchi-Levi; preliminary version appeared in ICML 2021.
• Offline Planning and Online Learning under Recovering Rewards, with Feng Zhu and David Simchi-Levi; preliminary version appeared in ICML 2021.
• Approximating Performance Measures for Slowly Changing Non-stationary Markov Chains, with Harsha Honnappa and Peter W. Glynn.
• Approximating Performance Measures for Slowly Changing Non-stationary Markov Chains, with Harsha Honnappa and Peter W. Glynn.
Teaching/Courses
Teaching/Courses
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