Xiaoqi Tan, PhD

Assistant Professor, Faculty of Science - Computing Science


Assistant Professor, Faculty of Science - Computing Science
3-03 Athabasca Hall
9119 - 116 St NW
Edmonton AB
T6G 2E8


Area of Study / Keywords

Online Algorithms Online Optimization Algorithmic Game Theory Mechanism Design Multi-Agent Systems Multi-Agent Learning


Xiaoqi Tan’s research focuses on developing new algorithms and markets in the general field of “optimization and decision-making under uncertainty" using mathematical tools from computer science, economics, operations research, control, etc. In particular, his lab studies the interplay between algorithms (for optimization), learning (for prediction), and incentives (for modeling strategic behaviors) in online/multi-agent decision-making, aiming to create a common framework for optimal decision-making under different forms of uncertainty (e.g., online/sequential inputs and strategic behaviors). On the practical side, his research is driven by optimization and decision-making problems in smart grid, electricity markets, urban transportation-energy nexus, cloud computing and data centers, etc.

For more details about Xiaoqi’s research, or if you are interested in joining his lab as an undergraduate, graduate, or postdoc, see his research website: SODALab.ca.


CMPUT 204 - Algorithms I

The first of two courses on algorithm design and analysis, with emphasis on fundamentals of searching, sorting, and graph algorithms. Examples include divide and conquer, dynamic programming, greedy methods, backtracking, and local search methods, together with analysis techniques to estimate program efficiency. Prerequisites: CMPUT 175 or 275 and CMPUT 272; one of MATH 100, 113, 114, 117, 134, 144, 154, or SCI 100.

CMPUT 605 - Topics in Computing Science

CMPUT 676 - Topics in Computing Science

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