About

I’m Min Wu. I work at the intersection of AI safety, alignment, evaluation, and governance — the disciplines concerned with making increasingly capable AI systems controllable, measurable, and accountable as they move into the real world.

My background is in mathematics. I hold a Ph.D. in mathematics from the University of Washington, where my research was in representation theory and noncommutative algebraic geometry. I’m also a CFA charterholder and a member of the CFA Society New York. That combination — abstract mathematical structure on one side, the discipline of financial risk on the other — is how I approach AI: rigorously, and with a bias toward the harder questions underneath the demos. How do we know a system is safe? How do we evaluate what it can really do? Who is accountable when it acts?

What I focus on

• AI safety — keeping powerful systems controllable, secure, and resistant to misuse.
• Alignment — making models pursue what we actually intend, not just what we literally specify.
• Evaluation — measuring capability, reliability, and safety in ways that hold up to scrutiny.
• Governance — frameworks, model risk, and accountability for AI systems, including how current model-risk guidance (SR 26-2) holds up against agentic AI.

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Disclaimer

The views and analyses expressed on this website are solely my own and are based on publicly available information unless otherwise noted. They do not represent the views of my employer, clients, or any affiliated organizations.

All articles are based on publicly available research papers, technical reports, regulatory documents, court filings, government publications, or other publicly accessible sources. No article relies on confidential or non-public information, or proprietary materials.