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.
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.
Hani is a software engineer at OpenAI.
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.
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.
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.
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.
Joseph works in Detections and Response at OpenAI. His public security work includes using large language models to detect malicious macOS activity.
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).
Jason is a Member of Technical Staff at OpenAI working on alignment and model behavior.
The MATS Program is a 10-week research fellowship designed to train and support emerging researchers working on AI alignment, transparency and security. Fellows collaborate with world-class mentors, receive dedicated research management support, and join a vibrant community in Berkeley focused on advancing safe and reliable AI. The program provides the structure, resources, and mentorship needed to produce impactful research and launch long-term careers in AI safety.
MATS mentors are leading researchers from a broad range of AI safety, alignment, governance, field-building and security domains. They include academics, industry researchers, and independent experts who guide scholars through research projects, provide feedback, and help shape each scholar’s growth as a researcher. The mentors represent expertise in areas such as:
Key dates
Application:
The main program will then run from September 28th to December 4th, with the extension phase for accepted fellows beginning in December.
MATS accepts applicants from diverse academic and professional backgrounds - from machine learning, mathematics, and computer science to policy, economics, physics, cognitive science, biology, and public health, as well as founders, operators, and field-builders without traditional research backgrounds. The primary requirements are strong motivation to contribute to AI safety and evidence of technical aptitude, research potential, or relevant operational experience. Prior AI safety experience is helpful but not required.
Applicants submit a general application, applying to various tracks (Empirical, Theory, Strategy & Forecasting, Policy & Governance, Systems Security, Biosecurity, Founding & Field-Building.
In stage 2, applicants apply to streams within those tracks as well as completing track specific evaluations.
After a centralized review period, applicants who are advanced will then undergo additional evaluations depending on the preferences of the streams they've applied to before doing final interviews and receiving offers.
For more information on how to get into MATS, please look at this page.