Find your fit in AI safety research
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MATS fellows work across seven tracks spanning technical research, governance, biosecurity, strategy, theory, security, and field-building. Explore each track to learn what fellows work on, who the track is for, and how to apply.
2026 Tracks
View MATS autumn 2026 tracks and their streams.
Founding and Field-Building
AI safety needs to scale fast, and the bottleneck is increasingly organizational. For founders, field-builders, and high-agency generalists launching new AI safety organizations, programs, and projects mentored by founders, sitting CEOs, and program directors across the ecosystem.
Empirical
Hands-on research using machine learning experiments to understand and improve model safety including AI control, interpretability, scalable oversight, evaluations, red-teaming, and robustness.
Policy and Governance
Research on how advanced AI is and should be governed, spanning governance mechanisms, regulatory and institutional analysis, and the technical systems that make governance enforceable.
Biosecurity
Research on catastrophic biological risk in a world being reshaped by advanced AI. Spans pathogen detection, medical countermeasures, synthesis screening, physical biodefense, threat modeling, and red-teaming biological AI for dangerous capabilities.
Strategy and Forecasting
Research on how AI development is likely to unfold and what that means for long-term safety. Includes timelines, takeoff dynamics, risk modeling, and strategic analysis of AI's trajectory.
Theory
Foundational research on the mathematical and philosophical principles underlying agency, alignment, and safe reasoning in advanced AI systems.
Systems Security
Research on software and hardware security for the infrastructure on which advanced AI runs, including side-channel analysis, cluster security, model-weight protection, and physical-layer verification.
