I am an Assistant Professor in Information Systems at Naveen Jindal School of Management, UT Dallas. I received my PhD degree in Information, Risk, and Operations Management from UT Austin. I received my master’s degree in Statistics from Univerisity of Michigan, and bachelor’s degree in Statistics from the School of the Gifted Young at University of Science and Technology of China (USTC). I previously worked at Netflix Research (advised by James Mclnerney and Nathan Kallus), Harvard University (advised by Himabindu Lakkaraju), IBM Research (advised by Wei Sun, Max Biggs, and Markus Ettl), Tencent, and Amazon.
Research
- How to develop algorithms that can better utilize human expertise to enhance machine learning systems?
- How to design more effective/ethical human-AI systems and AI explanations?
- How to assess individual preferences or algorithm performance in the presence of missing counterfactuals?
Recent News
- Received New Faculty Research Grant (2 out of 11)!
- Our paper HR-Bandit is accepted at AISTATS 2025!
- Our paper SEL-BALD was honored as the Best Paper Runner-Up (2nd out of 243 accepted papers) at WITS 2024!
- Our paper Confounding-Robost Deferral Policy Learning is accepted at AAAI 2025!
- Our Paper on Human-AI Collaborated Active Learning with Selective Labels is accepted at NeurIPS 2024!
- Our paper Adjusting Regression Models for Conditional Uncertainty Calibration is accepted at Machine Learning (SI on Uncertainty Quantification)!
- Our paper Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design is accepted at RealML @ NeurIPS, 2023!
- Two papers - Contextual Recourse Bandits and Confounding-Robust Policy Improvement with Human-AI Teams are accepted at CIST 2023 and INFORMS Data Science Workshop, 2023, respectively!
- Our paper On the Impact of Algorithmic Recourse on Social Segregation is accepted at ICML, 2023!
- Our paper Probabilistic Conformal Prediction Using Conditional Random Samples is accepted at AISTATS, 2023 (Preliminary version accepted as Spotlight presentation at ICML Distribution-Free Uncertainty Quantification, 2022)!
- Two papers - Active Incentive Learning / Robust Human-AI Collaboration with Bandit Feedback (Best Student Paper, 1 out of ~200 accepted papers) are accepted at CIST 2022!
Publications
* Equal Contribution
Selected Publications at ML/AI Conferences
J Cao*, R Gao*, E Keyvanshokooh*. HR-Bandit: Human-AI Collaborated Linear Recourse Bandit (AISTATS 2025).
R Gao, M Yin. Confounding-Robost Deferral Policy Learning (AAAI 2025).
R Gao, M Yin, M Saar-Tsechansky. SEL-BALD: Deep Bayesian Active Learning for Selective Labeling with Instance Rejection (NeurIPS 2024)
R Gao, H Lakkaraju. On the Impact of Algorithmic Recourse on Social Segregation (ICML 2023)
Z Wang*, R Gao*, M Yin*, M Zhou, D Blei. Probabilistic Conformal Prediction Using Conditional Random Samples (AISTATS 2023)
R Gao, M Biggs, W Sun, and L Han. Enhancing counterfactual classification performance via self-training (AAAI 2022)
R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee, M Lease. Human-AI Collaboration with Bandit Feedback (IJCAI 2021)
R Gao, M Saar-Tsechansky. Cost-accuracy aware adaptive labeling for active learning. (AAAI 2020)
L Han, R Gao, M Kim, X Tao, B Liu, D Metaxas. Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons. (AAAI 2020)
L Han, Y Zou, R Gao, L Wang, D Metaxas. Unsupervised domain adaptation via calibrating uncertainties. (CVPR Workshop 2019)
L Han, MR Min, A Stathopoulos, Y Tian, R Gao, A Kadav, D Metaxas. Dual Projection Generative Adversarial Networks for Conditional Image Generation. (ICCV 2021)
Journal Publications and Under Review at Journals
R Gao, M Yin, J McInerney, N Kallus. Adjusting Regression Models for Conditional Uncertainty Calibration (Machine Learning 2024)
[Under Review] R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee, W Sun, M Lease. Learning Complementary Policies for Human-AI Teams (Under review at Management Science - Reject and Resubmit, previous title: Robust Human-AI Collaboration with Bandit Feedback, Best Student Paper at CIST 2022)
[Under Review] M Biggs*, R Gao*, W Sun* Loss Functions for Discrete Contextual Pricing with Observational Data (Under review at Operations Research - Major Revision, spotlight presentation at RMP 2022, special recognition award finalist at ADA 2022).
Working Journal Papers
Manuscript will be shared upon request.
R Gao, M Saar-Tsechansky. Active Incentive Learning (Preliminary version accepted at CIST 2022). [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]
Patent
R Gao, W Sun, M Biggs, M Ettl, Y Drissi. Counterfactual Self-Training US Patent App. 17/402,367.
R Gao, W Sun, M Biggs, Y Drissi, M Ettl. Imputing Counterfactual Data to Facilitate Machine Learning Model Training US Patent App. 17/654,617.
Awards and Fellowships
- New Faculty Research Grant (2 out of 11).
- Best Paper Runner-Up Award at WITS 2024 (2nd out of 243 accepted papers).
- Best Student Paper Award at CIST 2022 (1 out of ~200 accepted papers).
- 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
- Journal: Management Science, Information Systems Research, MIS Quarterly, Operations Research, IJOC, TPAMI, Scientific Reports, ..
- ML/AI conference: ICLR, NeurIPS, ICML, FAccT, AISTATS, AAAI, WACV
- IS conference: WITS, ICIS, INFORMS Data Science Workshop