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.

Research

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?

News

• 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) are accepted at CIST 2022!
• Our paper Loss Functions for Discrete Contextual Pricing with Observational Data has been selected as a finalist for PhD Incubator Special Recognition Award at INFORMS Advances in Decision Analysis Conference!
• I will be visiting Harvard University this summer!
• Our paper Loss Functions for Discrete Contextual Pricing with Observational Data is accepted as Spotlight presentation at Revenue Management and Pricing conference, 2022!
• I received UT Austin Graduate School Fellowship for 2022-2023!
• Our paper Counterfactual Self-Training is accepted at AAAI, 2022! (Acceptance Rate: 15%)
• Our paper AE-StyleGAN is accepted at WACV, 2022!
• Our paper P2GAN is accepted at ICCV, 2021!
• Our paper Human-AI Collaboration with Bandit Feedback is accepted at IJCAI, 2021! (Acceptance Rate: 13.9%)

Preprint / Under Review

• R Gao, H Lakkaraju. On the Impact of Algorithmic Recourse on Social Segregation (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, previous title: Robust Human-AI Collaboration with Bandit Feedback, Best Student Paper at CIST, 2022)
• M Biggs*, R Gao*, W Sun* Loss Functions for Discrete Contextual Pricing with Observational Data (Under review, spotlight presentation at RMP 2022, special recognition award finalist at ADA 2022).
• R Gao, H Feng Identifying Best Fair Intervention

Selected Publications

• Z Wang*, R Gao*, M Yin*, M Zhou, D Blei Probabilistic Conformal Prediction Using Conditional Random Samples (AISTATS 2023, preliminary version accepted as spotlight presentation at ICML DFUQ, 2022).
• 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)

* 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..