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

My research focuses on advancing human-centered machine learning, with an emphasis on enhancing the robustness, interpretability, adaptability, and privacy of different kinds of ML/AI models, including foundational models. Many of my research aim to answer these questions:

How to develop algorithms that can work better with humans?
How to design more effective human-AI systems and AI explanations?
How to assess individual preferences or algorithm performance in the presence of missing counterfactuals?

I am looking for self-motivated undergraduate/graduate students who are interested in the broad area of human-centered ML/AI. Please send me an email with your CV and a brief description of your research interests if you are interested in working with me. For prospective PhD students, please apply to the IS PhD program at UT Dallas. Please note that I am unable to respond to individual inquiries regarding admissions to master’s or PhD programs.

The Jindal School of Management (JSOM) at UT Dallas is ranked #2 worldwide in research contributions and is recognized as a top 40 business school by U.S. News & World Report. The Information Systems (IS) PhD program has an excellent track record of placements, with recent graduates securing assistant professor positions at universities such as HKUST, Temple University, UIUC, University of Washington, and University of Maryland.

Publications

* Equal Contribution

Human-AI Collaboration and Generative AI

J Cao*, R Gao*, E Keyvanshokooh*.
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit AISTATS 2025
R Gao, M Yin.
Confounding-Robust 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 Best Paper Runner-Up at WITS 2024
R Gao, M Saar-Tsechansky, M De-Arteaga, L Han, MK Lee.
Human-AI Collaboration with Bandit Feedback IJCAI 2021
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
[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 Management Science - Major Revision Best Student Paper at CIST 2022
[Under Review] M Yin, R Gao, Z Cong.
Personalizing Language Models for Generative Targeting Marketing Science - Reject and ResubmitMarketing Science Institute Grant

Causal ML & Uncertainty Quantification

R Gao, M Yin.
Confounding-Robust Deferral Policy Learning AAAI 2025
R Gao, M Yin, J McInerney, N Kallus.
Adjusting Regression Models for Conditional Uncertainty Calibration Machine Learning 2024
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
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

Interpretability & Recourse

J Cao*, R Gao*, E Keyvanshokooh*.
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit AISTATS 2025
R Gao, H Lakkaraju.
On the Impact of Algorithmic Recourse on Social Segregation ICML 2023

Data Attribution/Evaluation & Active Learning

R Gao, M Yin, M Saar-Tsechansky.
SEL-BALD: Deep Bayesian Active Learning for Selective Labeling with Instance Rejection NeurIPS 2024 Best Paper Runner-Up at WITS 2024
R Gao, M Saar-Tsechansky.
Cost-accuracy aware adaptive labeling for active learning AAAI 2020
[Working Paper] R Gao, M Saar-Tsechansky.
Active Incentive Learning Preliminary version at CIST 2022
[Working Paper] Y Yang, R Gao, Z Zheng.
Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption. Preliminary version at CIST 2025 & INFORMS WDS 2025 Best Paper Award at INFORMS Workshop on Data Science 2025

Working Papers & Patents

Working Journal Papers

Manuscript will be shared upon request.

[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 Management Science - Major Revision Best Student Paper at CIST 2022
[Under Review] M Biggs*, R Gao*, W Sun*.
Loss Functions for Discrete Contextual Pricing with Observational Data Operations Research - Major Revision
[Under Review] M Yin, R Gao, Z Cong.
Personalizing Language Models for Generative Targeting Marketing Science - Reject and ResubmitMarketing Science Institute Grant
Y Yang, R Gao, Z Zheng.
Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption. Preliminary version at CIST 2025 & INFORMS WDS 2025 Best Paper Award at INFORMS Workshop on Data Science 2025
M Yin, R Gao, W Lin, S Shugan.
Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design Preliminary version at RealML @ NeurIPS 2023

Patents

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

Best Paper Award at INFORMS Workshop on Data Science 2025.
INFORMS ISS Nunamaker-Chen Dissertation Runner-Up Award.
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

Ruijiang Gao