I am a first-year Computer Science PhD student at Stanford Theory Group. My research focuses on the intersection of Computer Science and Economics, where I am interested in considering incentives to design computer systems with desired properties (e.g., efficiency, fairness. Part of my job is to identify and define these properties). My main approach utilizes theoretical techniques from optimization, probability, complexity, graph theory, etc. In the past few years, I had experience doing research in algorithmic information design, algorithmic auction design, matching markets, and mechanism design for blockchains.
I completed my undergraduate degree in Computer Science and Cognitive Science at University of Virginia, where I had the pleasure of working with Haifeng Xu (now at the Department of Computer Science and Data Science Institute at UChicago). After graduation, I spent a year working with Pinyan Lu as a visiting student at Shanghai University of Finance and Economics. I recently graduated with an M.S.E. in Computer Science from Princeton University, where I was very fortunate to be advised by Matt Weinberg. Check here for my CV.
Publications
Profitable Manipulations of Cryptographic Self-Selection are Statistically Detectable [arxiv]
(α-β) Linda Cai, Jingyi Liu, S. Matthew Weinberg, Chenghan Zhou
AFT 2024: The 6th International Conference on Advances in Financial Technologies.
Better Approximation for Interdependent SOS Valuations [arxiv]
(α-β) Pinyan Lu, Enze Sun, Chenghan Zhou
WINE 2022: The 18th Conference on Web and Internet Economics.
Information Design for Multiple Independent and Self-Interested Defenders: Work Less, Pay Off More [pdf]
Chenghan Zhou, Andrew Spivey, Haifeng Xu, Thanh Hong Nguyen
UAI 2022: The 38th Conference on Uncertainty in Artificial Intelligence (also accepted to Games Journal).
Algorithmic Information Design in Singleton Congestion Games [arxiv]
Chenghan Zhou, Thanh H. Nguyen, Haifeng Xu
EC 2022: Proc. 23th ACM Conference on Economics and Computation.