Xiaoqi Tan strives to create a common framework for optimal decision-making under different types of uncertainty with formal guarantees on performance, robustness and explainability.

Decision-making under uncertainty

Xiaoqi Tan is an Assistant Professor of Computing Science at the University of Alberta and a Fellow of Amii. He is also Faculty at SODA Lab at the University of Alberta.

Prior to July 2021, he was a Postdoctoral Fellow at the University of Toronto. He received his Ph.D. from the Hong Kong University of Science and Technology in 2018. During his Ph.D., he was also a visiting research fellow at Harvard University's School of Engineering and Applied Science.

Xiaoqi’s research interests span various topics in optimization and decision-making under uncertainty, with an emphasis on exploring the interplay between algorithms (for optimization), learning (for prediction), and incentives (for modeling strategic behaviors) in online/multi-agent decision-making.