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 fairness 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 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?

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

Selected Preprints

R Gao, M Yin. Confounding-Robust Policy Improvement with Human-AI Teams (Preliminary version accepted at INFORMS Data Science Workshop 2023).
J Cao*, R Gao*, E Keyvanshokooh*. HR-Bandit: Human-AI Collaborated Linear Recourse Bandit (Preliminary version accepted at CIST 2023; INFORMS 2024).

Selected Publications at ML/AI Conferences

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

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

Journal: ISR, OR, IJOC, TPAMI, Scientific Reports
ML/AI conference: ICLR, NeurIPS, ICML, FAccT, AISTATS, AAAI, WACV
IS conference: WITS, ICIS, INFORMS Data Science Workshop

Ruijiang Gao