I am a researcher at METR.
I think the development of AI is going to be a confusing time for the world. I want to help provide good evidence and methodologies for tracking AI development and risk, so humanity can make sensible decisions.
I've had different roles at different times, including leading task development and our monitoring stream. I like prototyping new kinds of evaluations. I think it's healthy to read transcripts. I'm interested in what capabilities matter for being a competent agent, and why current AI agents fall short. I feel lucky that I get to spend time building an understanding of the models.
I've previously spent time at the Centre on Long-Term Risk and FHI. Before that I studied physics at university, where I did malaria diagnostics research.
Peter is an assistant professor at Princeton University, where he works on reinforcement learning, alignment, and law. He received a J.D. and Ph.D. in computer science from Stanford University.
Cristian is a Research Fellow at Artificial Intelligence Underwriting Company (AIUC). Insurers have been known to play the role of private regulators (such as in commercial nuclear power); his work broadly focuses on how we might steer the insurance market for AI toward an effective private governance regime.
He was previously a Winter Fellow at the Centre for the Governance of AI, and an independent researcher at the AI Safety Student Team at Harvard. He has an M.A. in Philosophy from the University of British Columbia.
Milad is a research scientist at Anthropic studying how language models affect computer security. Before joining Anthropic, Milad researched AI security and privacy at OpenAI and Google DeepMind.
I research AI safety and alignment at Anthropic. Before that, I was a research scientist at Google DeepMind. I completed my PhD at UC Berkeley's Center for Human-Compatible AI, advised by Stuart Russell. I previously cofounded FAR.AI, a 501(c)3 research nonprofit that incubates and accelerates beneficial AI research agendas.
I develop AI alignment frameworks, stress-test their limits, and turn insights into methodology adopted across the field. I have established that chain-of-thought monitoring is a substantial defense when reasoning is necessary for misalignment, designed practical metrics to preserve monitorability during model development, shown that obfuscated activations can bypass latent-space defenses, and developed StrongREJECT, a jailbreak benchmark now used by OpenAI, US/UK AISI, Amazon, and others.
Krishnamurthy (Dj) Dvijotham is a senior staff research scientist at Google DeepMind, where he leads efforts on the development of secure and trustworthy AI agents. He previously founded the AI security research team at ServiceNow Research and co-founded the robust and verified AI team at DeepMind. His past research has received best paper awards at many leading AI conferences, including most recently at ICML and CVPR 2024. His research led to the framework used for AI security testing at ServiceNow and has been deployed in several Google products, including the Android Play Store, YouTube and Gemini.
I am an interpretability researcher at Anthropic. I am most interested in simple, practical interpretability approaches that are targeted at making models safer. In a previous life, I worked as a neuroscientist.
Seth is a senior advisor on research and policy at SecureBio, where he works on AI and biosecurity. He previously completed a Ph.D. at Harvard and postdoctoral research at the University of Chicago.
I was until recently a professional mathematician at the University of Melbourne, where I worked on algebraic geometry, mathematical logic, some aspects of mathematical physics, and most recently statistical learning theory. As of early 2025 I left academia to direct research at Timaeus on AI safety.
Saad Siddiqui is a senior researcher at Safe AI Forum, where his research examines possible agreement between leading AI powers. He previously worked as a management consultant at Bain and Company in Singapore.
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.