Max Springer

Postdoctoral Research Fellow, Center for Information Technology Policy, Princeton University

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About

I am a postdoctoral research fellow at Princeton University's Center for Information Technology Policy. I study algorithmic infrastructure: the algorithms and AI systems that quietly make consequential decisions in elections, hiring, markets, and public institutions. Rather than waiting for these systems to fail, I work to inspect and reinforce them in advance.

My research comes at this from three sides. I design algorithms with provable fairness and efficiency guarantees; I develop methods to measure, attack, and verify the alignment of large language models; and I build agentic AI systems for research, along with the verifiable checks that keep them honest, including THEO, a multi-agent system that formalizes research mathematics in Lean, and TCS-Bench, a benchmark of frontier models' research ability. I also stress-test real public systems against increasingly capable AI. Most recently, I showed that a publicly available AI assistant could turn a known flaw in Georgia's voting system into a practical way to match voters to their ballots, work presented at DEF CON 34 and covered by the Associated Press.

Beyond research, I am a scientific consultant to the United Nations Independent International Scientific Panel on AI, where I helped synthesize the panel's Preliminary Report, and I write about mathematics and computer science for Scientific American as a former AAAS Mass Media Fellow.

I received my Ph.D. in Applied Mathematics from the University of Maryland, advised by MohammadTaghi Hajiaghayi and supported by an NSF Graduate Research Fellowship, and have held research positions at Google Research, Nokia Bell Labs, and the Max Planck Institute for Informatics. Earlier, I worked in computational biology, building machine-learning models of consciousness from EEG and fMRI data at Yale and models of insulin and glucose dynamics at the NIH.

I am on the 2026–27 job market.