Research Lead

Adversarial Robustness: working to rigorously solve security problems through building a science of security and robustness for AI, from demonstrating superhuman systems can be vulnerable https://far.ai/post/2023-07-superhuman-go-ais/, to scaling laws for robustness https://www.far.ai/news/does-robustness-improve-with-scale and jailbreaking constitutional classifiers https://arxiv.org/abs/2506.24068.

Mechanistic Interpretability: finding https://arxiv.org/abs/2502.12892 issues with Sparse Autoencoders, probing deception using AmongUs , understanding learned planning in SokoBan, and interpretable data attribution.