Nian Si
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I am an assistant professor at HKUST IEDA. I study optimization and evaluation of decisions. I was a postdoctoral principal researcher at the University of Chicago Booth School of Business, working with Professor Baris Ata and Professor J. Michael Harrison . I obtained my Ph.D. in the Department of Management Science and Engineering (MS&E) at Stanford University, where I was advised by Professor Jose Blanchet and closely worked with Professor Ramesh Johari. I was a member of the Stanford Operations Research Group. Previously, I obtained a B.A. in Economics and a B.S. in Mathematics and Applied Mathematics both from Peking University in 2017. My research lies at the interface of operations research, statistics, and machine learning. I am also interested in real-world operational problems arising from online platforms, including A/B tests, recommendation systems, online advertising, AI, etc. AdvertisementI am looking for undergraduate or graduate students with:
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Contact
Email: niansi at ust dot hk
Room 5559F
Department of Industrial Engineering and Decision Analytics
The Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
Education
Ph.D. in Operations Research, Stanford University, 09/2017 - 01/2023
Ph.D. minor in Computer Science, Stanford University, 09/2021 - 01/2023
M.S. in Statistics, Stanford University, 01/2020 - 03/2021
B.A. in Economics, Peking University, 09/2013 - 07/2017
B.S. in Mathematics and Applied Mathematics, Peking University, 09/2014 - 07/2017
Selected Recent Papers
Experimental Design in Live-Interaction Platforms
Chenran Weng, Xiao Lei, Nian Si
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
Nian Si
On the Foundation of Distributionally Robust Reinforcement Learning
Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications
Baris Ata, Michael Harrison, Nian Si, Queueing Systems: Theory and Applications (QUESTA) , 2024
Confidence Regions in Wasserstein Distributionally Robust Estimation
Jose Blanchet, Karthyek Murthy, Nian Si, Biometrika, 2021
Research Interests
Reinforcement learning
Experimentation design and causal inference
Distributional robustness and transfer learning
Stochastics and simulation