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’s degree in Statistics at Univerisity of Michigan.
Ex-Intern at HBS, IBM Research, Tencent, and Amazon.


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?


Preprint / Under Review

Selected Publications

* Equal Contribution

Working Paper

Manuscript will be shared upon request.

  • R Gao, M Saar-Tsechansky. Active Incentive Learning (Preliminary version accepted at CIST, 2022)

Professional Service

Program Commitee Member and Reviewer for FAccT 2023, AISTATS 2023, AAAI 2023, WITS 2022, CIST 2022, WACV 2022, INFORMS Data Science Workshop, ICML workshops on Adversarial Machine Learning Frontiers, ML for Data, Human-Machine Collaboration and Teaming..