Prof. Chen Xinyun

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Prof. Chen Xinyun WEBSITE

Biography

Xinyun Chen is currently an Assistant Professor in Insitute for Data and Decision Analytics, 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, Monte Carlo method and their applications in financial markets. She has published papers in journals including Annals of Applied Probability, Mathematics of Operations Research and Accounting and Finance. Before joining iDDA, she was an Assistant Professor at Stony Brook University and Wuhan University.

Academic Publications

Articles in Journals


  1. Two-parameter Sample Path Large Deviations for Infinite Server Queues, with Jose Blanchet and Henry Lam (2014). Stochastic Systems, 4:206-249.

  2. Steady-state simulation of reflected Brownian motion and related stochastic networks, with Jose Blanchet (2015). Annals of Applied Probability, 25:3209-3250.

  3. 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.

  4. Does the T+1 Rule Really Reduce Speculation? Evidence from Chinese Stock Index ETF, with Yan Liu and Tao Zeng (2018), Accounting and Finance, 57:1287–1313.

  5. 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.

  6. Perfect Sampling for Generalized Jackson Networks, with Jose Blanchet, Mathematics of Operations Research, forthcoming.

Conference Proceedings


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

  2. 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.