About Me
I am a PhD student in Computer Science at the University of Toronto. My research lies at the intersection of computational social choice, algorithmic fairness, and AI alignment.
I study how groups with different preferences can make collective decisions efficiently and fairly. My current work focuses on voting rules, preference elicitation, fair clustering, and the preservation of minority viewpoints in learning systems.
Publications
Publications are listed in reverse chronological order. See my CV for additional details.
2026The Art of Calling the Winner by Asking Just Enough Questions
Nisarg Shah and Ziqi Yu
Conference on Uncertainty in Artificial Intelligence (UAI 2026), Oral Presentation.
2026Fair Division with Prioritized Agents
Xiaolin Bu, Zihao Li, Shengxin Liu, Jiaxin Song, Biaoshuai Tao, and Ziqi Yu
Information and Computation, 2026.
2025Unifying Proportional Fairness in Centroid and Non-Centroid Clustering
Benjamin Cookson, Nisarg Shah, and Ziqi Yu
Advances in Neural Information Processing Systems (NeurIPS 2025), Spotlight Poster.
2025On the Existence of EFX (and Pareto-Optimal) Allocations for Binary Chores
Biaoshuai Tao, Xiaowei Wu, Ziqi Yu, and Shengwei Zhou
Theoretical Computer Science, 1042:115248, 2025.
2025How Likely Are Two Voting Rules Different?
Ziqi Yu, Lirong Xia, Qishen Han, and Chengkai Zhang
The 41st Conference on Uncertainty in Artificial Intelligence (UAI 2025).
2023EFX Allocations Exist for Binary Valuations
Xiaolin Bu, Jiaxin Song, and Ziqi Yu
International Workshop on Frontiers in Algorithmics (FAW 2023), pp. 252-262.
Research Interests
- Computational Social Choice: voting rules, smoothed analysis, preference elicitation, and committee selection.
- Fair Clustering: group representation, core fairness, and approximation algorithms.
- Pluralistic AI: heterogeneous preferences, alignment-data selection, and minority-opinion preservation.
Contact
Department of Computer Science
University of Toronto
Email: yuziqi53@gmail.com
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