I am an assistant professor at HKUST IEDA. I study decision evaluation and understanding to improve decision learning. 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.

Contact

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

• Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
  Shengbo Wang, Nian Si
  

• 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
  

• 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