About me

I am currently a PhD candidate at McCombs School of Business, UT Austin advised by Maytal Saar-Tsechansky. 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 Netflix Research (advised by James Mclnerney and Nathan Kallus), HBS (advised by Himabindu Lakkaraju), IBM Research (advised by Wei Sun, Max Biggs, and Markus Ettl), Tencent, and Amazon.


My research interests include the broad area of robust machine learning, reinforcement learning (the specific area of contextual bandit), active learning, AI ethics 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 design better human-AI systems and comprehend their sociotechnical implications?


Under Review at Journals

Selected Publications at ML/AI Conferences

Working Journal Papers

Manuscript will be shared upon request.

  • R Gao, M Saar-Tsechansky. Active Incentive Learning (Preliminary version accepted at CIST, 2022). [Abstract]
  • R Gao, M Yin. Confounding-Robust Policy Improvement with Human-AI Teams (Preliminary version accepted at INFORMS Data Science Workshop, 2023). [Abstract]
  • J Cao*, R Gao*, E Keyvanshokooh*. Contextual Recourse Bandits: Optimizing Decisions through Counterfactual Explanations (Preliminary version accepted at CIST, 2023). [Abstract]
  • M Yin, R Gao, W Lin, S Shugan. Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design (Preliminary version is accepted at RealML @ NeurIPS, 2023). [Abstract]


* Equal Contribution

Awards and Fellowships

  • Best Student Paper Award at CIST 2022 (1 out of ~200).
  • PhD Incubator Special Recognition Award at INFORMS Advances in Decision Analysis Conference.
  • INFORMS Data Science Workshop Scholarship 2022, 2023.
  • UT Austin Continuing Fellowship 2022-2023 (competitive fellowship, one nomination per department).
  • UT Austin Graduate School (OGS) Professional Development Award, Good Systems Student Conference Grant.
  • UT Austin Graduate School (OGS) Provost Fellowship.

Professional Service

Program Committee Member and Reviewer for ICLR 2024, WITS 2023, ICIS 2023, NeurIPS 2023, FAccT 2023, AISTATS 2023/2024, AAAI 2023/2024, WITS 2022, CIST 2022, WACV 2022, INFORMS Data Science Workshop, NeurIPS Workshop on Regulatable ML, ICML workshops on Interactive Learning from Human Feedback, Generative AI + Law, Adversarial Machine Learning Frontiers, ML for Data, Human-Machine Collaboration and Teaming..