Xiaoqi Tan, PhD

Area of Study / Keywords

Optimization Decision-making under uncertainty Online algorithms Algorithmic game theory Mechanism design Multi-agent systems

Research

Xiaoqi Tan’s research interests span various topics in online algorithms, algorithmic game theory, and learning theory. The main theme of his research is to develop algorithms that optimize decision-making under uncertainty, leveraging mathematical tools from computer science, economics, statistics, and control.

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 lab page: https://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

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