Xiaoqi Tan, PhD

Area of Study / Keywords

online algorithms decision-making under uncertainty algorithmic economics artificial intelligence systems and theory energy sustainability

Research

Xiaoqi Tan’s research focuses on developing algorithmic and economic tools for optimization and decision-making under uncertainty. In particular, his work tackles resource allocation problems characterized by (i) the need to make sequential decisions before the full input is known and (ii) the private and unknown preferences of strategic agents. Real-world examples of such resources include slots in online advertising, distributed energy resources in electrical grids, CPU and memory in cloud computing, and bandwidth in communication networks.

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

The SODALab@UofA is constantly looking for highly motivated graduate students at both the MSc and PhD level. If you are already at UofA and want to join us, feel free to email your CV to Xiaoqi. For prospective students who are not currently enrolled at UofA, read Xiaoqi's Letter to Prospective Students first before reaching out.

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; and one of MATH 100, 114, 117, 134, 144, or 154.

CMPUT 676 - Topics in Computing Science

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