Xinyun Chen is currently an Assistant Professor in the Schoo of Data Science at The Chinese University of Hong Kong, Shenzhen. She received her Ph.D in Operations Research from Columbia University in 2014. Her research interests include applied probability, stochastic simulation, queueing theory and reinforcement learning. She has published papers in journals including Annals of Applied Probability, Mathematics of Operations Research and Accounting and Finance. Before joining CUHK-SZ, she was an Assistant Professor at Stony Brook University and Wuhan University.

Professional Services:

Council Member, INFORMS Applied Probability Society, 2019-2021

Editor, Applied Probability Trust, 2020-

Cluster Chair, INFORMS Annual Meeting, 2020

Programe Committee, Performance, 2020


* denotes industry/medical collaborator; underline denotes Ph.D student

Working Papers

An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems, with Yunan Liu and Guiyu Hong, submitted to Operations Research. [arXiv]

Data-Pooling Reinforcement Learning for Personalized Healthcare Intervention, with Pengyi Shi and Xiuwen Wang. (A short version of the paper is accepted to Reinforcement Learning for Real Life Workshop @ ICML 2021). [pdf, video].

Journal Papers

Two-parameter Sample Path Large Deviations for Infinite Server Queues, with Jose Blanchet and Henry Lam (2014). Stochastic Systems, 4:206-249.
Steady-state simulation of reflected Brownian motion and related stochastic networks, with Jose Blanchet (2015). Annals of Applied Probability, 25:3209-3250.
ε-Strong Simulation for Multidimensional Stochastic Differential Equations via Rough Path Analysis, with Jose Blanchet and Jing Dong (2017). Annals of Applied Probability, 27:275-336.
Does the T+1 Rule Really Reduce Speculation? Evidence from Chinese Stock Index ETFwith Yan Liu and Tao Zeng (2018), Accounting and Finance, 57:1287–1313.
Many-server Gaussian limits for overloaded non-Markovian queues with customer abandonment, with A. Korhan Aras and Yunan Liu (2018), Queueing Systems: Theory and Applications, 89(1): 81-125.
Perfect Sampling for Generalized Jackson Networks, with Jose Blanchet (2019), Mathematics of Operations Research, 44(2): 693-714.
Rates of Convergence to Stationarity for Reflected Brownian Motion, with Jose Blanchet (2020), Mathematics of Operations Research, 45(2):660-681.

Perfect Sampling of Hawkes Processes and Queues with Hawkes Arrivals, accepted to Stochastic Systems. [arXiv]

Efficient Steady-state Simulation of High-dimensional Stochastic Networks, with Jose Blanchet, Peter Glynn and Nian Si (2020), Stochastic Systems, 11(2):174-192. [pdf]

Conference Proceedings

Exact gradient simulation for stochastic fluid networks in steady state. Simulation Conference (WSC), 2014 Winter(Vol.2015, pp.586-594). IEEE.

Modeling inter-trade durations in the limit order market, with Jianzhao Yang, Zhicheng Li and Haipeng Xing. 2016 Symposium of the International Chinese StatisticalAssociation Series: Springer Proceedings in Mathematics & Statistics, Vol. 57. Springer-Verlag, New York.

Infinite-horizon off-policy policy evaluation with multiple behavior policies, with Lu Wang, Yizhe Hang, Heng Ge and Hongyuan Zha. ICLR 2020.

Perfect Sampling of Multi-dimensional Hawkes Process, with Xiuwen Wang, accepted to Simulation Conference (WSC) 2020. [arXiv]

A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations, with Yihan Pan, Zhenghang Xu, Jin Guang, Jingjing Sun, Chengwenjian Wang, Xuanming Zhang, Jim Dai, Yichuan Ding, Pengyi Shi, Hongxin Pan*, Kai Yang* and Song Wu*, accepted to Simulation Conference (WSC) 2021. [arXiv]