I am a computer science Ph.D. candidate at Princeton University's Center for Information Technology Policy and a Porter Ogden
Jacobus Fellow. Previously, I was a senior fellow at Mozilla
and a Laurance
S. Rockefeller Graduate Prize Fellow at Princeton University. I co-authored AI Snake Oil with Arvind Narayanan, named one of Nature's 10 best books of 2024. My
current work focuses on AI evaluation science, AI as Normal Technology, evidence-based AI policy,
and reproducibility in machine-learning-based science. I am a recipient of a best paper award at ACM
FAccT, an impact recognition award at ACM CSCW, and was included in TIME's inaugural list of the 100 most
influential people in AI.
I am working on my next book with Arvind Narayanan on AI as Normal Technology.
AI evaluation science
I study how to evaluate AI agents and frontier systems with reliable, inspectable, and realistic evidence.
AI as Normal Technology
Analyzing AI as transformative but normal technology, not superintelligence.
Evidence-based AI policy
I work on AI policy grounded in evidence about model openness, evaluation access, transparency, and accountability.
-
Open foundation models:
Science: governance considerations for open foundation models.
ICML oral: societal impacts of open foundation models. - FMTI: A longitudinal effort to measure transparency across leading foundation model developers: 2025 · 2024 · 2023
- Testimonies and input: ASAP RFI · New Jersey Assembly · Congressional Forum
Reproducibility
I study how ML-based science fails to reproduce and what practices, benchmarks, and reporting standards can make computational research more credible.
- REFORMS: Consensus recommendations for more reliable ML-based science.
- Leakage: How data leakage drives the reproducibility crisis in ML-based science.
- CORE-Bench: A benchmark for computational reproducibility agents.
- Replicability prediction: Replicability prediction limits in scientific claims.
- Worst of both worlds: Shared error modes in psychology and machine learning.