Toby Webster is a program director at Sentinel Bio, leading work on risks at the intersection of AI and engineering biology. Previously, Webster studied biological AI model security at RAND Europe.
David Demitri Africa is a research scientist at Resolution, where he works on understanding the character and motivations of AI systems in ways that scale to superintelligence.
Alfie is the founder and research lead at Formation Research, a startup research organisation focusing on lock-in and power concentration risks from AI systems. He works on AI-enabled totalitarianism, coups, and extreme power concentration. He claims that AI systems can make future versions of these phenomena much more stable, long-term, harmful, and widespread, and is focusing on developing and implementing governance-informed interventions for these problems leveraging technical methods of verification and cooperation.
Jan worked as a software developer for over a decade before shifting to AI safety in 2023. He is an ARENA and Astra Fellowship alumni, interested in anything related to out-of-context reasoning in LLMs.
Caspar Oesterheld is a researcher at Redwood Research where he works on improving models' conceptual reasoning capabilities, i.e., their reasoning about questions where we cannot verify the answer and the best way to make progress is through argumentation. Much of this work has been done in collaboration with Anthropic.
Previously, he completed his computer science PhD at Carnegie Mellon University where he was assistant director of the Foundations of Cooperative AI Lab. Caspar has published about multi-agent AI interactions, decision theory in Newcomb-like decision problems, and, informally, about how models reason about conceptual questions. He has served as a research mentor for MATS, PIBBSS, CLR and astra (incoming).
Neil Chowdhury is a member of technical staff at Transluce, a research lab building tools for understanding AI systems. Chowdhury previously worked on safety at OpenAI.
McKenna Fitzgerald leads external affairs at Americans for Responsible Innovation (ARI), an AI policy and advocacy organization based in Washington, D.C. She was previously a Research Manager at MATS where she helped initiate the technical governance stream. Prior to MATS, she was Deputy Director of the Global Catastrophic Risk Institute. She is a Board Member of Magnify Mentoring and an Advisory Board Member of PRISM Research. She holds a B.A. in philosophy from UC, Berkeley.
I'm an AI safety grantmaker at Longview Philanthropy and a part-time DPhil student in AI at Oxford. Previously I did research on machine learning and AI policy at Epoch, GovAI, and the Center for AI Safety. Before that, I worked on data science at a fintech startup.
I have founded three successful businesses and sold two, and now I've turned to investing in AI Safety.
Over the years I've learned a lot about what it takes to actually manage and run an organization, focus on what matters and scale the things that are important.
Alex Kleinman is a co-founder of Active Site. Previously, Kleinman worked on broad-spectrum vaccines at Alvea.
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.