— 'Grounded Persuasive Language Generation for Automated Marketing' (preprint, 2024)
— 'Auctioning with Strategically Reticent Bidders' (WINE 2024)
— 'Generalized Principal-Agency: Contracts, Information, Games and Beyond' (WINE 2024)
— 'Robust Stackelberg Equilibria' (EC 2023, Major Revision at Mathematical Programming)
Background
PhD student at Sigma Lab, University of Chicago, advised by Prof. Haifeng Xu
Research lies at the intersection of game theory, learning theory, and optimization, focusing on multi-agent decision-making under complex and unknown environments
Aims to advance design principles of intelligent systems toward 'strategic alignment'—aligning stakeholder interests for mutually beneficial outcomes
Interested in developing practical techniques to: 1) build incentive-aware AI agents with strategic intelligence and rationalizable behaviors; 2) align economic incentives among users, model developers, and data providers for sustainable AI ecosystems