Lucas is a program operations associate on Coefficient Giving’s Technical AI Safety team. He previously worked as an associate consultant at Bain & Company and holds a Master in Public Policy from Harvard Kennedy School.
Max Nadeau is a Program Officer on Coefficient Giving's Technical AI Safety team. Previously, he conducted research on machine-learning robustness and interpretability.
Greg Kollmer is a co-founder of Lucid Computing, which develops secure, verifiable AI infrastructure. As a Columbia engineering graduate student Kollmer co-developed Palmos, a wireless sensor network for early landslide detection.
Jake Mendel is a technical AI safety program officer at Coefficient Giving, where he makes grants for technical work. Previously, he worked as a research scientist at Apollo Research.
I am a scientist working at the intersection of artificial intelligence and neuroscience. My career began with a focus on understanding how the brain enables perception and behavior, using computational models to connect neural activity with cognition. After nearly a decade of academic research, including a PhD at Stanford University and a postdoctoral fellowship at Harvard Medical School, I have transitioned into industry to apply these insights more broadly.
I am currently a Researcher at OpenAI, where I focus on two goals: advancing safety research to ensure artificial intelligence systems are reliable and aligned, and exploring how AI can accelerate progress in health and medicine. I am especially motivated by the opportunity to translate my background in neuroscience and modeling into building AI tools that both deepen scientific discovery and contribute to human well-being.
At the core of my work is a curiosity about intelligent systems—both biological and artificial—and a commitment to using that understanding to create technologies that are safe, responsible, and transformative.
Manu Shivakumara is a senior bioinformatics engineer at IBBIS, where he supports the development and optimization of the Common Mechanism, an open-source software platform for DNA synthesis screening. Shivakumara previously researched environmental DNA datasets at ETH Zurich and holds a PhD in genomics.
I am a Senior Research Fellow at the Center for the Governance of AI, leading a work stream that investigates national security threats from advanced AI systems. I am also a collaborator at METR, where I help improve the rigor of system cards and evals, and a senior at the Forecasting Research Institute.
I am interested in mentoring projects that create rigorous threat models of near-term AI misuse, especially within biosecurity. Given that this work can include sensitive topics, the final output might look like writing memos and briefings for decision-makers instead of academic publications.
I am also interested in projects that try to strengthen the science and transparency of dangerous capability evaluations reporting. This includes creating standards and checklists, writing peer reviews of model cards, and designing randomized control trials that can push the current frontier.
Keri is the technical lead of the Infrastructure Security Engineering Team at Anthropic, implementing SL4/5 and searching for differentially defense-favored security tools.
George Robinson is an independent researcher formerly at the Alignment Research Center (ARC), working on a systematic and theoretically grounded approach to mechanistic interpretability. He is now looking to lead a research effort in London supporting this agenda. Previously, he was a PhD student at Oxford University specialising in Algebraic Number Theory. He lives in London, and is a member of the London Initiative for Safe AI (LISA).
Zainab is the co-founder of Asymmetric Security. She was previously a cybersecurity analyst at Stroz Friedberg, where she investigated some of the largest cybersecurity breaches of the past decade (e.g., Cambridge Analytica). She has also published at NeurIPS on AI cybersecurity evaluations. Zainab holds a master’s degree in Physics from Oxford University.
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.