I am an Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin. My primary research focus is on data-driven decision-making and artificial intelligence.

‪Assistant Professor, University of Texas at Austin‬ - ‪‪Cited by 2,013‬‬ - ‪Operations Research‬

Rui Gao is an Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin. His primary research focus is on data-driven decision-making and artificial intelligence.

Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels, NeurIPS, 2021. Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators, NeurIPS, 2021. Robust Hypothesis Testing Using …

Rui Gao is an assistant professor of IROM in the McCombs School of Business at The University of Texas at Austin. Gao’s main research studies data-driven decision making under uncertainty and prescriptive data analytics, and he teaches undergraduate courses …

Proceedings of the European Conference on Computer Vision (ECCV) Workshops … R Gao, F Wan, D Organisciak, J Pu, J Wang, H Duan, P Zhang, X Hou, ...

Rui GAO, Professor (Assistant) | Cited by 529 | of University of Texas at Austin, TX (UT) | Read 21 publications | Contact Rui GAO

Assistant Professor in Decision Science at The University of Texas at Austin · https://faculty.mccombs.utexas.edu/rui.gao/ · Experience: McCombs School of Business, The University of Texas at...

2024年4月19日 · Dr. Rui Gao is an Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin.

Journal Articles [A1] Luhao Zhang†, Jincheng Yang†, and Rui Gao. A short and general duality proof for Wasserstein distributionally robust optimization. Operations Research, (Forthcoming), 2024. [A2] Hao Wang†, Rui Gao, and Flavio P Calmon. Generalization bounds for noisy iterative algorithms using properties of additive noise channels.