Rate My Professor Fang-Yi Yu

Fang-Yi Yu is an Assistant Professor in the Department of Computer Science within the College of Engineering and Computing at George Mason University. Yu received a PhD in Computer Science and Engineering from the University of Michigan, advised by Grant Schoenebeck. Before joining George Mason University, Yu was a postdoctoral fellow at the Harvard John A. Paulson School of Engineering and Applied Sciences, hosted by Yiling Chen, and a Postdoctoral Research Fellow at the School of Information at the University of Michigan, hosted by Grant Schoenebeck.

Yu's research is broadly situated at the interface between machine learning, artificial intelligence, and economics. Current research focuses largely on multi-agent systems interacting with information, including information elicitation and aggregation mechanisms and multi-agent learning. Yu has published peer-reviewed papers in leading conferences such as AAAI, NeurIPS, EC, SODA, AAMAS, WWW, ITCS, WINE, and FORC. Key publications include "Optimally Auditing Adversarial Agents" (AAAI 2026, with Sanmay Das, Yuang Zhang), "Algorithmic Robust Forecast Aggregation" (EC 2025, with Yongkang Guo, Jason D. Hartline, Zhihuan Huang, Yuqing Kong, Anant Shah), "Hardness and Approximation Algorithms for Balanced Districting Problems" (FORC 2025, with Prathamesh Dharangutte, Jie Gao, Shang-En Huang), "Designing Automated Market Makers for Combinatorial Securities: A Geometric Viewpoint" (SODA 2025, with Prommy Sultana Hossain, Xintong Wang), "Carrot and Stick: Eliciting Comparison Data and Beyond" (NeurIPS 2024, with Yiling Chen, Shi Feng), "Optimal Scoring Rule Design under Partial Knowledge" (WINE 2024, with Yiling Chen), "Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos" (EC 2023, with Georgios Piliouras), "Differentially Private Network Data Collection for Influence Maximization" (AAMAS 2023, with M. Amin Rahimian, Carlos Hurtado), "Integer Subspace Differential Privacy" (AAAI 2023, with Prathamesh Dharangutte, Jie Gao, Ruobin Gong), "Peer Prediction for Learning Agents" (NeurIPS 2022, with Shi Feng, Yiling Chen), "A System-Level Analysis of Conference Peer Review" (EC 2022, with Yichi Zhang, Grant Schoenebeck, David Kempe), "Subspace Differential Privacy" (AAAI 2022, with Jie Gao, Ruobin Gong), "Information Elicitation from Rowdy Crowds" (WWW 2021, with Grant Schoenebeck, Yichi Zhang), and "Timely Information from Prediction Markets" (AAMAS 2021, with Grant Schoenebeck, Chenkai Yu).