Jason Stanley
Head of Applied AI Research
AI Research Leadership
Jason Stanley is Head of Applied AI Research. Previously he led the company’s applied research team working on AI trust and governance. In the past, he has led research and product teams in technology companies, has served on working groups on AI issues at the OECD, the Partnership on AI, ML Commons, and other organizations. Jason spent several years working on strategic labor market policy for the Government of Canada. He holds a Ph.D. in Sociology from New York University and social science degrees from Oxford University and Williams College.
Publications
Societal Alignment Frameworks Can Improve LLM Alignment.
ACM Conference on Fairness, Accountability, and Transparency,
2026.
No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms.
International Conference on Learning Representations,
2026.
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats.
Conference on Language Modeling (COLM),
2025.
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats.
Workshop at the International Conference of Machine Learning (ICML),
2025.
Silent Sabotage: Injecting Backdoors into AI Agents Through Fine-Tuning.
Workshop at the International Conference of Machine Learning (ICML),
2025.
Societal Alignment Frameworks Can Improve LLM Alignment.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
LitLLMs, LLMs for Literature Review: Are We There Yet?.
Transactions on Machine Learning Research (TMLR),
2025.