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.

My research sits at the intersection of reinforcement learning, algorithmic game theory, and combinatorial optimization — developing tractable algorithms and mathematical insights for constrained and multi-agent decision-making under uncertainty. My results enable near-optimal agents that obey safety constraints and operate robustly, even in NP-hard and adversarial domains.

More broadly, I am interested in principled ways to combine machine learning and worst-case algorithm design to create capable and reliable AI agents.