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

Seth Donoughe
RAND
,
Director of AI
—

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.

Focus:
Empirical
Capability and Propensity Evaluations, Adversarial Robustness and Safeguards, Biosecurity
Eric Winsor
UK AISI
,
Research Engineer
—

Eric Winsor is a research scientist at the UK AI Security Institute and contributes to adversarial testing of frontier AI model safeguards. Winsor earned a B.S.E. in computer engineering from the University of Michigan.

Focus:
Empirical
AI Control and Monitoring, Capability and Propensity Evaluations, Adversarial Robustness and Safeguards
Daniel Murfet (Dan)
Timaeus
,
Director of Research
—

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.

Focus:
Empirical
Alignment Training Methods, Interpretability, Theoretical Alignment and Formal Methods
Saad Siddiqui
Safe AI Forum
,
Senior AI Policy Researcher
—

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.

Focus:
Policy and Governance
Policy and Governance, Technical AI Governance
Jesse Hoogland
Timaeus
,
Executive Director
—

Jesse is the co-founder and executive director of Timaeus, an AI safety non-profit researching applications of singular learning theory (SLT) to AI safety, particularly for interpretability and alignment. Jesse comes from a background in physics and leads several research projects at Timaeus, in addition to being involved in outreach and operations.

Focus:
Empirical
Misalignment Science, Interpretability
Pierre-Luc St-Charles
LawZero
,
Senior ML Research Scientist
—

Pierre-Luc St-Charles is a researcher and developer specializing in applied machine learning with over a decade of experience across different non-profit institutes. He has held research roles at the Computer Research Institute of Montréal and senior research roles at Mila, collaborating with industrial partners and multidisciplinary academic teams on innovative projects in natural resources, transportation, digital media, document intelligence, and earth observation. Pierre-Luc earned his PhD in Computer Vision from Polytechnique Montréal in 2018, receiving the departmental Best Thesis Award. In 2024, he joined LawZero, a Mila-incubated organization focused on developing safe AI technologies. He is currently focused on building benchmarks and evaluation methodologies for frontier AI systems.

Focus:
Empirical
AI Control and Monitoring, Capability and Propensity Evaluations, Adversarial Robustness and Safeguards, Agent Foundations

Isabella is a Senior Researcher at the Safe AI Forum, where she works on U.S.–China coordination on frontier AI safety. Her research focuses on technical governance, particularly building consensus and advancing dialogue on loss-of-control and extreme-misuse risks, with recent work spanning technical misuse safeguards, AI control playbook, and misalignment incidents. She holds an MA in Computational Social Science from the University of Chicago and a BS in Philosophy, Politics, and Economics from University College London.

Focus:
Policy and Governance
Policy and Governance, Technical AI Governance
Marc-Antoine Rondeau
LawZero
,
Senior Machine Learning Research Scientist
—

Marc-Antoine is a Research Scientist at LawZero. His main area of expertise is NLP and applied ML, and he is currently applying this to AI safety projects.

His research areas include interpretability and evaluation.

Focus:
Empirical
AI Control and Monitoring, Capability and Propensity Evaluations, Interpretability, Agent Foundations
Ryan Tang
Rutgers University
,
Tenure-Track Assistant Professor
—

Ruixiang (Ryan) Tang is an assistant professor of computer science at Rutgers University–New Brunswick and leads the Trustworthy and Reliable AI Lab, whose work spans operational interpretability, agent safety and control, and perception reliability. Tang earned a Ph.D. in computer science from Rice University and a B.S. from Tsinghua University.

Focus:
AI Control and Monitoring, Adversarial Robustness and Safeguards, Interpretability

Frequently asked questions

What is the MATS Program?
Who are the MATS Mentors?
What are the key dates of the MATS Program?
Who is eligible to apply?
How does the application and mentor selection process work?