https://elliott-chen.github.io
Yi Chen
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Ph.D. Candidate Contact:Email: yichen2016@u.northwestern.edu |
About me
I am a fifth year Ph.D. student at Department of Industrial Engineering and Management Sciences, Northwestern University, advised by Prof. Zhaoran Wang and Prof. Jing Dong. My research interests are at the interface of operations research, machine learning, and applied econometrics. Currently, my research primarily focuses on developing advanced algorithms with provable efficiency to facilitate better operational decision making. I also study consumer behavior via machine learning and econometrics tools and aim to understand the implications of customer behavior on operational strategies. Previously, I obtained my Bachelor degree in Statistics from University of Science and Technology of China.
In the upcoming 2020 INFORMS annual meeting, I am going to present two of my recent works. The first one is ‘‘A Primal-Dual Approach to Constrained Markov Decision Processes’’, which is scheduled at 02:00 PM - 03:15 PM, 11132020, virtual room 03. The other one is ‘‘epsilon-Strong Simulation of Fractional Brownian Motion and Related Stochastic Differential Equations’’, which is scheduled at 04:30 PM - 05:45 PM, 11132020, virtual room 31. You are sincerely welcomed to attend and your comments will be greatly appreciated.
Research
Working Papers:
Wang Y.*, Chen Y.*, Fang E., Wang Z. and Li R., Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection, under review at Journal of the American Statistical Association
Chen Y., Dong J. and Wang Z., A Primal-dual Approach to Constrained Markov Decision Processes, in preparation for submission
Chen Y., Dong J. and Zheng F., Understanding Consumer Search Behavior on Large-scale Retail Platform, working in progress
Publications:
Chen Y., Dong J. and Ni H. (2020), epsilon-Strong Simulation of Fractional Brownian Motion and Related Stochastic Differential Equations, forthcoming in Mathematics of Operations Research
Chen Y., Chen J., Dong J., Peng J. and Wang Z. (2019), Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion, 2019 International Conference on Learning Representations
Chen Y. and Dong J. (2019), On the Almost Sure Convergence Rate for a Series Expansion of Fractional Brownian Motion, Proceedings of the 2019 Winter Simulation Conference
Chen Y., Yang Z., Xie Y. and Wang Z. (2018), Contrastive Learning from Pairwise Measurements, 2018 Advances in Neural Information Processing Systems
* equal contribution
Awards
Terminal Year Fellowship (2020), Graduate School, Northwestern University
Arthur P. Hurter Award (2017), Department of IEMS, Northwestern University
The 35th Guo Moruo Award (2016, highest honor for undergraduate), University of Science and Technology of China
Exceptional Graduate Award (2016), University of Science and Technology of China
National Scholarship (2015), Ministry of Education of P.R. China
Industry Experience
Alibaba, Seattle, WA (Jun-Sep, 2019): Research Scientist Intern, working on demand estimation.
Teaching Experience
Teaching Assistant: IEMS 303 Statistics, IEMS 315 Stochastic Models, IEMS 313 Foundations of Optimization, IEMS 343 Project Management, IEMS 345 Negotiations for Engineers and IEMS 461 Stochastic Processes I (Ph.D. core).