MATS mentors are advancing the frontiers of AI alignment, transparency, and security

Jean-Pierre Falet
LawZero; Université de Montréal
,
Machine Learning Research Scientist

Jean-Pierre is a machine learning research scientist at LawZero, focused on designing model-based AI systems with quantitative safety guarantees. His primary interests are in probabilistic inference in graphical models, and he draws inspiration from his multidisciplinary background in neurology and neuroscience, which informs his understanding of human cognition. Jean-Pierre studied at McGill University, obtaining a medical degree in 2017, completing a neurology residency in 2022, and earning a master's degree in neuroscience in 2023. During his master’s, he developed causal machine learning methods for precision medicine. Concurrently with his work at LawZero, Jean-Pierre is completing a PhD in computer science at Mila and Université de Montréal, supervised by Yoshua Bengio. In addition to contributing to the foundations of guaranteed-safe AI, Jean-Pierre is passionate about translating advances in AI into clinically meaningful, safety-critical applications.

Focus:
Empirical
AI Control and Monitoring, Agent Foundations, Theoretical Alignment and Formal Methods
Dylan Sam
OpenAI
,
Member of Technical Staff

Dylan is a safety researcher at OpenAI, where he works on curating better/safer training data and monitoring models for harmful behavior.

Before that, he completed a PhD in the Machine Learning Department at CMU.

Focus:
Empirical
AI Control and Monitoring, Alignment Training Methods
Hani Mir
OpenAI
,
Software Engineer

Hani is a software engineer at OpenAI.

Focus:
Empirical
AI Control and Monitoring, Capability and Propensity Evaluations, Adversarial Robustness and Safeguards, Misalignment Science, Alignment Training Methods, Interpretability, AI Systems Security
Byron Cohen
RAND
,
Biosecurity Fellow

Byron Cohen is an AI and Biosecurity Research Resident at RAND, where he works on AI-biosecurity risk assessment and policy research. Previously, he served as a biosecurity advisor at DARPA’s Biological Technologies Office, where he advised on biosurveillance, attribution, epidemiological modeling, and AI:bio uplift risk. Before that, he served as Advisor for Interagency R&D Oversight at the White House Office of Pandemic Preparedness and Response Policy (OPPR). An epidemiologist by training, he holds a PhD in population health sciences from Harvard University, and has conducted peer-reviewed epidemiological modeling research on biosafety and global health security.

Focus:
Biosecurity
Capability and Propensity Evaluations, Adversarial Robustness and Safeguards, Policy and Governance, Biosecurity
Isak Czeresnia Etinger
OpenAI
,
Member of Technical Staff

Isak is a Member of Technical Staff at OpenAI. Previously a Software Engineer at Google, he worked on applications of computer vision, natural language processing, and LLMs.

Isak earned a Master of Computer Science at Carnegie Mellon University, with published work in natural language processing, style transfer, multilingual grapheme-to-phoneme modeling, and computer vision.

Focus:
Empirical
AI Control and Monitoring, Capability and Propensity Evaluations, Adversarial Robustness and Safeguards, Misalignment Science, Alignment Training Methods, Interpretability, AI Systems Security
Bijan Varjavand
OpenAI
,
Technical Program Manager

Bijan is a Technical Program Manager at OpenAI. He previously worked as a research engineer at Scale AI, where he coauthored work on LLM jailbreaking and red-teaming workflows.

Focus:
Empirical
Adversarial Robustness and Safeguards

My focus these days is on adversarial machine learning: safety, security, and alignment of frontier models. I am particularly interested in alignment/safety RL and evaluations. In the past, I studied memorization, privacy, and security harms in language modelling, including auditing for risks and mitigating them. I've also worked on DP training algorithms, unlearning, collaborative learning approaches, and methods for ownership-verification.

Focus:
Empirical
Capability and Propensity Evaluations, Adversarial Robustness and Safeguards, Alignment Training Methods, AI Systems Security
Joseph Millman
OpenAI
,
Member of Technical Staff (Detections and Response)

Joseph works in Detections and Response at OpenAI. His public security work includes using large language models to detect malicious macOS activity.

Focus:
Empirical
AI Systems Security
Maja Trebacz
OpenAI
,
Member of Technical Staff

Maja is a researcher at OpenAI, working on techniques for improving control and alignment as AI systems become more capable and agentic. Her team’s work combines longer-horizon research with hands-on deployment. They study long-term questions about how increasingly intelligent systems can be supervised, constrained, and corrected, while also building oversight systems that are used in practice today, both internally and externally (see recent work on code review and action monitoring for codex).

Focus:
Empirical
AI Control and Monitoring, Alignment Training Methods
Jason Wolfe
OpenAI
,
Member of Technical Staff

Jason is a Member of Technical Staff at OpenAI working on alignment and model behavior.

Focus:
Empirical
Misalignment Science, Alignment Training Methods

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