(* indicates a supervised student or postdoc co-author.)

Preprints

• Yifan Lin*, Yuhao Wang*, Enlu Zhou, “Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate“, submitted.
• Yingke Li, Mengxue Hou, Enlu Zhou, and Fumin Zhang, “Dynamic Event-triggered Integrated Task and Motion Planning for Process-aware Source Seeking”, submitted.
• Alexander Shapiro, Enlu Zhou, Yifan Lin*, Yuhao Wang*, “Episodic Bayesian Optimal Control with Unknown Randomness Distributions“, submitted.
• Mengxue Hou, Tony Lin, Enlu Zhou, Fumin Zhang, “Mori-Zwanzig Approach for Belief Abstraction with Application to Belief Space Planning”, submitted.
• Yingke Li, Yifan Lin*, Enlu Zhou, Fumin Zhang, “Bayesian Risk-averse Model Predictive Control with Consistency and Stability Guarantees”, submitted.
• Yuhao Wang* and Enlu Zhou, “Data-driven Optimal Computing Budget Allocation under Input Uncertainty“, submitted.
• Yifan Lin*, Yuxuan Ren*, Jingyuan Wan*, Massey Cashore, Jiayue Wan, Yujia, Peter Frazier, and Enlu Zhou, “Group Testing during the COVID-19 Pandemic: Optimal Group Size Selection and Prevalence Control”.
• Tianyi Liu* and Enlu Zhou, “Online Quantification of Input Model Uncertainty by Two-Layer Importance Sampling”, submitted.
• Di Wu* and Enlu Zhou, “Analyzing and Provably Improving Fixed Budget Ranking and Selection Algorighms”, submitted.
• Chang-Han Rhee*, Enlu Zhou, and Peng Qiu, “Space-filling Design for Nonlinear Models”, submitted.

2024

• Yingke Li, Enlu Zhou, and Fumin Zhang, “A Distributed Bayesian Data Fusion Algorithm with Uniform Consistency”, accepted, IEEE Transactions on Automatic Control, 2024.

2023

• Sait Cakmak*, Yuhao Wang*, Siyang Gao, and Enlu Zhou, “Contextual Ranking and Selection with Gaussian Processes and OCBA “, accepted, ACM Transanctions on Modeling and Computer Simulation (special issue on ISIM 2021), 2023.
• Tianyi Liu*, Yifan Lin*, and Enlu Zhou, “Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data“, accepted, SIAM Journal on Optimization, 2023.
• Gongbo Zhang, Yijie Peng, Jianghua Zhang, and Enlu Zhou, “Asmptotically Optimal Sampling Policy for Selecting Top-m Alternatives”, INFORMS Journal on Computing, 2023.
• Alexander Shapiro, Enlu Zhou, and Yifan Lin*, “Bayesian Distributionally Robust Optimization“, SIAM Journal on Optimization, 2023.
• Yuhao Wang* and Enlu Zhou, “Bayesian Risk-Averse Q-Learning with Streaming Observations“, Advances in Neural Information Processing Systems (NeurIPS), 2023. ( INFORMS DMDA Workshop Best Paper Award Runner-up (Theoretical Track))
• Yingke Li, Ziqiao Zhang, Junkai Wang, Huibo Zhang, Enlu Zhou, Fumin Zhang, “Cognition Difference-based Dynamic Trust Network for Distributed Bayesian Data Fusion”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.
• Yuhao Wang* and Enlu Zhou, “Bayesian Risk-Averse Q-Learning with Streaming Data”, International Conference on Machine Learning (ICML) workshop on “PAC-Bayes Meets Interactive Learning”, 2023.
• Yingke Li, Mengxue Hou, Enlu Zhou, and Fumin Zhang, “Integrated Task and Motion Planning for Process-aware Source Seeking,” American Control Conference (ACC), 2023.
• Yuhao Wang* and Enlu Zhou, “Input Data Collection versus Simulation: Simultaneous Resource Allocation”, Winter Simulation Conference (WSC), 2023.
• Yifan Lin* and Enlu Zhou, “Reusing Historical Observations in Natural Policy Gradient”, Winter Simulation Conference (WSC), 2023.

2022

• Yifan Lin*, Yuhao Wang*, and Enlu Zhou, “Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs“, Journal of Systems Science and Systems Engineering, 2022.
• Di Wu*, Yuhao Wang*, and Enlu Zhou, “Data-driven Ranking and Selection under Input Uncertainty”, Operations Research, 2022.
• Yuhao Wang* and Enlu Zhou, “Fixed Budget Ranking and Selection under Streaming Input Data”, Winter Simulation Conference (WSC), 2022. (WSC Best Theoretical Paper Award)
• Yifan Lin*, Yuxuan Ren*, and Enlu Zhou, “Bayesian Risk Markov Decision Processes“, Advances in Neural Information Processing Systems (NeurIPS), 2022.
• Sam Daulton, Sait Cakmak*, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy, “Robust Multi-Objective Bayesian Optimization under Input Noise”, International Conference on Machine Learning (ICML), spotlight presentation, 2022.
• Tianyi Liu*, Yan Li, Enlu Zhou, Tuo Zhao, “Noise Regularizes Over-Parameterized Rank One Matrix Recovery, Provably”, Artificial Intelligence and Statistics (AISTATS), oral presentation, 2022.
• Yingke Li, Yifan Lin*, Enlu Zhou, and Fumin Zhang, “Risk-Aware Model Predictive Control Enabled by Bayesian Learning”, American Control Conference (ACC), 2022.

2021

• Tianyi Liu*, Zhehui Chen, Enlu Zhou, and Tuo Zhao, “A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization”, Stochastic Systems, 2021.
• Yingke Li, Tianyi Liu*, Enlu Zhou, and Fumin Zhang, “Bayesian Learning Model Predictive Control for Process-Aware Source Seeking”, IEEE Control Systems Letters, 2021.
• Sait Cakmak*, Di Wu, and Enlu Zhou, “Solving Bayesian Risk Optimization via Nested Stochastic Gradient Estimation”, IISE Transcations, 2021.
• Sait Cakmak*, Siyang Gao, and Enlu Zhou, “Contextual Ranking and Selection with Gaussian Processes”, Winter Simulation Conference (WSC), 2021.
• Tianyi Liu*, Yifan Lin*, and Enlu Zhou, “A Bayesian Approach to Online Simulation Optimization with Streaming Input Data”, Winter Simulation Conference (WSC), 2021.
• Yingke Li, Tianyi Liu*, Enlu Zhou, and Fumin Zhang, “Bayesian Learning Model Predictive Control for Process-Aware Source Seeking”, IEEE Conference on Decision and Control (CDC), 2021.
• Tianyi Liu*, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao, “Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization”, Artificial Intelligence and Statistics (AISTATS), 2021.

2020

• X. Yang, C.M. Tipton, M.C. Woodruff, E. Zhou, F.E.-H. Lee, I. Sanz, P. Qiu, “GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data”, BMC Genomics, 21(Suppl 9):583, 2020.
• Joshua Hale*, Helin Zhu*, and Enlu Zhou, “Domination Measure: A New Metric For Solving Multiobjective Optimization“, INFORMS Journal on Computing, 2020.
• Helin Zhu*, Tianyi Liu*, and Enlu Zhou, “Risk Quantification in Stochastic Simulation under Input Uncertainty“, ACM Transactions on Modeling and Computer Simulation, 2020.
• Sait Cakmak*, Rahul Astudillo, Peter Frazier, and Enlu Zhou, “Bayesian Optimization of Risk Measures“, Advances in Neural Information Processing Systems (NeurIPS), 2020.
• Tianyi Liu* and Enlu Zhou, “Simulation Optimization by Reusing Past Replicaitons: Don’t be afraid of Dependence”, Winter Simulation Conference (WSC), 2020. (WSC Best OR/MS-focused Student Paper Award)
• Yifan Lin*, Enlu Zhou, and Aly Megahed, “A Nested Simulation Optimization Approach for Portfolio Selection”, Winter Simulation Conference (WSC), 2020.

2019

• Di Wu* and Enlu Zhou, “Fixed Confidence Ranking and Selection under Input Uncertainty”, in Proceedings of the 2019 Winter Simulation Conference, 2019.
• Tianyi Liu*, MInshuo Chen, Mo Zhou, Simon Du, Enlu Zhou, and Tuo Zhao, “Towards Understanding the Importance of Shortcut Connections in Residual Networks”, in Advances in Neural Information Processing Systems 33 (NeurIPS 2019) Proceedings, 2019.
• Mo Zhou, Tianyi Liu*, Yan Li, Dachao Lin, Enlu Zhou, and Tuo Zhao, “Towards Understanding the Importance of Noise in Training Neural Networks”, in International Conference on Machine Learning (ICML) Proceedings, oral presentation, 2019.

2018

• Jenny E. Jeong, Nicholas J. Frohberg, Enlu Zhou, Todd Sulchek, and Peng Qiu, “Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices”, PLOS ONE, 2018.
• Jenny E. Jeong, Qinwei Zhuang, Mark K. Transtrum, Enlu Zhou, and Peng Qiu, “Experimental Design and Model Reduction in Systems Biology”, Quantitative Biology, 2018.
• Di Wu*, Helin Zhu*, and Enlu Zhou, “A Bayesian Risk Approach to Data-driven Stochastic Optimization: Formulations and Asymptotics“, SIAM Journal on Optimization, 2018. (2020 INFORMS Outstanding Simulation Publication Award)
• Xi Chen*, Enlu Zhou, and Jiaqiao Hu, “Discrete Optimization via Gradient-based Adaptive Stochastic Search Methods“, IISE Transactions, 2018. [software available here] [slides] [Matlab code]
• Tianyi Liu*, Shiyang Li, Jianping Shi, Enlu Zhou, and Tuo Zhao,”Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization“, in Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Proceedings, 2018.
• Enlu Zhou, and Tianyi Liu*, “Online Quantification of Input Uncertainty for Parametric Models”, in Proceedings of the 2018 Winter Simulation Conference, 2018.
• Di Wu*, and Enlu Zhou, “Analyzing and Provably Improving the Optimal Computing Budget Allocation Algorithm”, in Proceedings of the 2018 Winter Simulation Conference, 2018.

2017

• Enlu Zhou and Di Wu, “Simulation Optimization under Input Model Uncertainty”, Advances in Modeling and Simulation: Seminal Research from 50 Years of Winter Simulation Conferences, Springer, 2017. Editors: Andreas Tolk, John Fowler, Guodong Shao, and Enver Yucesan.
• Helin Zhu*, Joshua Hale*, and Enlu Zhou, “Simulation Optimization of Risk Measures with Adaptive Risk Levels“, Journal of Global Optimization, 2017.
• Enlu Zhou and Shalabh Bhatnagar, “Gradient-based Adaptive Stochastic Search for Simulation Optimization over Continuous Space“, INFORMS Journal on Computing, 2017.
• Henry Lam and Enlu Zhou, “The Empirical Likelihood Approach to Quantifying Uncertainty in Sample Average Approximation“, Operations Research Letters, 2017.
• Helin Zhu*, Fan Ye*, and Enlu Zhou, “A Regression Approach to Dual Problems in Dynamic Programming“, IEEE Transactions on Automatic Control, 2017.
• Siyang Gao, Hui Xiao, Enlu Zhou, and Weiwei Chen, “Robust Ranking and Selection with Optimal Computing Budget Allocation“, Automatica, 2017.
• Fan Ye*, Helin Zhu*, and Enlu Zhou, “Weakly Coupled Dynamic Program: Information and Lagrangian Relaxations“, IEEE Transactions on Automatic Control, 2017.
• Di Wu*, and Enlu Zhou, “Ranking and Selection under Input Uncertainty: a Budget Allocation Formulation”, in Proceedings of the 2017 Winter Simulation Conference, 2017.
• Helin Zhu*, Fan Ye* and Enlu Zhou, “An Efficient Regression Approach to Solving the Dual Problems of Dynamic Programs”, in Proceedings of the 20th IFAC World Congress, 2017.

2016

• Joshua Hale*, Enlu Zhou, and Jiming Peng, “A Lagrangian Search Method for the P-Median Problem“, Journal of Global Optimization, 2016. (Finalist for the Best Paper Published in Journal of Gloabal Optimization in 2016)
• Helin Zhu*, Joshua Hale*, and Enlu Zhou, “Optimizing Conditional Value-at-Risk via Gradient-based Adaptive Stochastic Search”, in Proceedings of the 2016 Winter Simulation Conference, 2016.
• Siyang Gao, Hui Xiao, Enlu Zhou, and Weiwei Chen, “Optimal Computing Budget Allocation with Input Uncertainty”, in Proceedings of the 2016 Winter Simulation Conference, 2016.

2015

• Jiaqiao Hu and Enlu Zhou, “On the Implementation of a Class of Stochastic Search Algorithms”, pp. 427-435, Advances in Global Optimization, Springer Proceedings in Mathematics & Statistics, 2015.
• Xi Chen* and Enlu Zhou, “Population Model-based Optimization”, Journal of Global Optimization, 2015.
• Fan Ye* and Enlu Zhou, “Information Relaxation and Dual Formulation of Controlled Markov Diffusions“, IEEE Transactions on Automatic Control, 2015.
• Helin Zhu*, Fan Ye*, and Enlu Zhou, “Fast Estimation of True Bounds on Bermudan Option Prices under Jump-diffusion Processes”, Quantitative Finance, 2015.
• Enlu Zhou and Wei Xie, “Simulation Optimizaion when Facing Input Uncertainty“, in Proceedings of the 2015 Winter Simulation Conference, 2015.
• Henry Lam and Enlu Zhou, “Quantifying Uncertainty in Sample Average Approximation“, in Proceedings of the 2015 Winter Simulation Conference, 2015.
• Helin Zhu* and Enlu Zhou, “Risk Assessment for Input Uncertainty with Budget Allocation”, in Proceedings of the 2015 Winter Simulation Conference, 2015.
• Joshua Hale* and Enlu Zhou, “A Model-based Approach to Multi-objective Optimization”, in Proceedings of the 2015 Winter Simulation Conference, 2015.
• Yi Yuan, Wei Xie, and Enlu Zhou, “A Sequential Experiment Design for Input Uncertainty Quantification in Stochastic Simulation”, in Proceedings of the 2015 Winter Simulation Conference, 2015.

2014

• Jiaqiao Hu, Enlu Zhou, and Qi Fan, “Model-based Annealing Random Search with Stochastic Averaging”, ACM Transactions on Modeling and Computer Simulation, 2014.
• Enlu Zhou and Jiaqiao Hu, “Gradient-based Adaptive Stochastic Search for Non-differentiable Optimization”, IEEE Transactions on Automatic Control, 2014. [software available here] [slides] [Matlab code]
• Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “Particle Filtering Framework for a Class of Randomized Optimization Algorithms”, IEEE Transactions on Automatic Control, 2014.
• Chang-Han Rhee*, Enlu Zhou, and Peng Qiu, “An Iterative Algorithm for Sampling from Manifolds“, in Proceedings of the 2014 Winter Simulation Conference, 2014.
• Enlu Zhou, Shalabh Bhatnagar, and Xi Chen*, “Simulation Optimization via Gradient-based Stochastic Search“, in Proceedings of the 2014 Winter Simulation Conference, 2014.
• Fan Ye* and Enlu Zhou, “Dual Formulation of Controlled Markov Diffusions and Its Application”, in Proceedings of the 19th IFAC World Congress, 2014.

Prior to 2014

• Fan Ye* and Enlu Zhou, “Optimal Stopping of Partially Observable Markov Processes: A Filtering-based Duality Approach”, IEEE Transactions on Automatic Control, 2013.
• Enlu Zhou, “Optimal Stopping under Partial Observation: Near-Value Iteration”, IEEE Transactions on Automatic Control, 2013.
• Qing-Shan Jia, Enlu Zhou, and Chun-Hung Chen, “Efficient Computing Budget Allocation for Finding Simplest Good Designs”, IIE Transactions, 2013.
• Shen Yan*, Enlu Zhou, and Chun-Hung Chen, “Efficient Selection of a Set of Good Enough Designs with Complexity Preference”, IEEE Transactions on Automation Science and Engineering, 2012.
• Enlu Zhou and Xi Chen*, “Sequential Monte Carlo Simulated Annealing”, Journal of Global Optimization, 2013. [Matlab code]
• Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “Solving Continuous-state POMDPs via Density Projection”, IEEE Transactions on Automatic Control, 2010.
• Alicia E. Meuret, David Rosenfield, Frank H. Wilhelm, Enlu Zhou, Ansgar Conrad, Thomas Ritz, and Walton T. Roth, “Do Unexpected Panic Attacks Occur Spontaneously?”,Biological Psychiatry, 2011.
• David Rosenfield, Enlu Zhou, Frank H. Wilhelm, Ansgar Conrad, Walton T. Roth, and Alicia E. Meuret, “Change Point Analysis for Longitudinal Physiological Data: Detection of Cardio-Respiratory Changes Preceding Panic Attacks”, Biological Psychology, 2010.
• Jiaqiao Hu, Yongqiang Wang, Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “A Survey of Some Model-Based Methods for Global Optimization”, pp. 157-180, Optimization, Control, and Applications of Stochastic Systems, in honor of Onésimo Hernández-Lerma, 2012. Editors: Hernández-Hernández, Daniel; Minjárez-Sosa, Adolfo.
• Xi Chen* and Enlu Zhou, “Population Model-based Optimization with Sequential Monte Carlo”, in Proceedings of the 2013 Winter Simulation Conference, 2013.
• Helin Zhu*, Fan Ye*, and Enlu Zhou, “True Upper Bounds on Bermudan Option Prices Under Jump-diffusion Processes”, in Proceedings of the 2013 Winter Simulation Conference, 2013.
• Enlu Zhou and Jiaqiao Hu, “Combining Gradient-based Optimization with Stochastic Search”, in Proceedings of the 2012 Winter Simulation Conference.
• Fan Ye* and Enlu Zhou, “Parameterized Penalties in the Dual Representation of Markov Decision Processes”, in Proceedings of the 51^stIEEE Conference on Decision and Control, 2012.
• Fan Ye* and Enlu Zhou, “Pricing American Options under Partial Observation of Stochastic Volatility”, in Proceedings of the 2011 Winter Simulation Conference, 2011.
• Shen Yan*, Enlu Zhou, and Chun-Hung Chen, “Efficient Simulation Budget Allocation for Selecting the Best Set of Simplest Good Enough Designs”, in Proceedings of the 2010 Winter Simulation Conference, 2010.
• Enlu Zhou and Xi Chen*, “A New Population-Based Simulated Annealing Algorithm”, in Proceedings of the 2010 Winter Simulation Conference, 2010.
• Xi Chen* and Enlu Zhou, “Simulation Method for Solving Hybrid Influence Diagrams in Decision Making”, in Proceedings of the 2010 Winter Simulation Conference, 2010.
• Enlu Zhou, Kun Lin, Michael C. Fu, and Steven I. Marcus, “A Numerical Method for Financial Decision Problems under Stochastic Volatility”, in Proceedings of the 2009 Winter Simulation Conference, 2009. (WSC Best Theoretical Paper Award)
• Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “A Particle Filtering Framework for Randomized Optimization Algorithms”, in Proceedings of the 2008 Winter Simulation Conference, 2008. (Finalist for the “Best Student Paper” Award)
• Enlu Zhou, Michael C. Fu, and Steven I. Marcus, “A Density Projection Approach to Dimension Reduction for Continuous-State POMDPs”, in Proceedings of 47^th IEEE Conference on Decision and Control, 2008.
• Yuxiao Hu, Zheng Hua, and Enlu Zhou, “Fly with Confidence”, the UMAP Journal, 2003. (INFORMS Outstanding Paper, Mathematical Contest in Modeling)