Jeremy McMahan

Hi! I am a Computer Science PhD graduate from the University of Wisconsin-Madison, where I was advised by Jerry Zhu and worked closely with Qiaomin Xie and Yudong Chen.

I am broadly interested in algorithms under uncertainty, with a focus on bridging the gap between classical combinatorial structures and data-driven decision making. Many of the problems and techniques I study lie at the intersection of Combinatorial Optimization, Game Theory, and Reinforcement Learning.

My research seeks to overcome intractability in modern sequential decision-making domains. To address this challenge, I have designed provably efficient approximation algorithms that guarantee agents can operate safely, robustly, and fairly—even in the face of uncertain inputs.