About me

I am currently a PhD candidate at McCombs School of Business, UT Austin.
I received my bachelor’s degree in Statistics at School of the Gifted Young, University of Science and Technology of China and master degree in Statistics at Univerisity of Michigan.


My research interests include the broad area of robust machine learning, reinforcement learning (the specific area of contextual bandit), active learning and causal inference. Many of my research aim to answer these questions:

  1. How to develop algorithms that can better utilize human expertise to enhance machine learning systems or aid human decisions?
  2. How to assess individual preference or algorithm performance in the presence of missing counterfactuals?
  3. How to build robust models with non-stationary environment?



* Equal Contribution


Working Paper

Manuscript will be shared upon request.

  • R Gao, M Saar-Tsechansky. Active Incentive Learning (Preliminary version accepted at CIST, 2022)
  • R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee, W Sun, M Lease. Robust Human-AI Collaboration with Bandit Feedback (Best Student Paper at CIST, 2022)