MATS Autumn 2026

We are interested in mentoring projects in AI forecasting and governance. This work would build on the AI 2027 report to either do more scenario forecasting or explore how to positively affect key decision points, informed by our scenario.

The Alignment Research Center is a small non-profit research group based in Berkeley, California, that is working on a systematic and theoretically grounded approach to mechanistically explaining neural network behavior. We are interested in scholars with a strong math background and mathematical maturity. If you'd be excited to work on the research direction described in this blog post – then we'd encourage you to apply!

This stream is primarily focused on research into physical defenses against engineered pathogens, aiming to inform decisions about PPE stockpiling and distribution approaches, improve improvised PPE and bioshelter scale-up, and reach rapid conclusions on how much to prioritize other areas of physical biodefense (agriculture, emergency response, etc.).  We are also open to strategic research into the use of bioweapons by AI or AI-human teams as part of takeover strategies and how this might inform preparedness.

This coalition of mentors make up the “Anthropic Stream”. This stream spans a range of empirical research areas in AI safety on LLMs, including AI control, scalable oversight, model organisms, model internals, model welfare, security, and more. You’ll be pitched, and have the option to pitch, a variety of safety research projects, and then be matched to projects and mentors based on your interests/preferences on research and what you’d like to get out of MATS. Fellows in this stream frequently receive funding and continued mentorship after MATS to complete their research project, usually leading to a (co-)first author paper. People in this stream often end up in long-term homes for safety research after MATS (e.g. Anthropic, Redwood Research, OpenAI).

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Anthropic mentors share an application, tend to collaborate and co-mentor projects together, and generally share infrastructure to streamline the fellow experience. By applying to this stream, you are being considered for all of the Anthropic mentors.

This stream focuses on building realistic defensive cybersecurity benchmarks utilizing data from Asymmetric Security's work on real-world incidents.

Founding ambitious AI safety and field-building projects.

I'm mentoring projects that apply AI advances to core biosecurity challenges — early detection and attribution of biological threats, characterizing AI-enabled bioweapons uplift, accelerating medical countermeasure design, and building biosecurity-by-design into frontier biological AI tools. Fellows have wide latitude to scope their own project within (or adjacent to) these themes, including ideas I haven't yet considered, and are expected to drive the technical work independently. My comparative advantage is high-level strategic direction grounded in biosecurity, pandemic preparedness, epidemiology, and US R&D policy rather than hands-on ML or software engineering guidance.

This mentor also has a stream in the Strategy and Forecasting track

This stream focuses on how advanced AI could enable new and dangerous bio technologies, and on assessing when risks become tractable or urgent as those capabilities arrive.

This mentor also has a stream in the Biosecurity track.

This stream focuses on how advanced AI could enable new and dangerous physical technologies, and on assessing when risks become tractable or urgent as those capabilities arrive.

I have two broad areas.

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Security:

I am interested in building demonstrations for hacking real-world AI deployments to show that they are not secure. The goal is to force companies to invest in alignment techniques that can solve the underlying security issues.

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Benchmarks:

I am interested in building benchmarks to determine how generalizable modern LLM techniques actually are, now that we are no longer in the pre-training scaling era.

This stream will focus on monitoring, stress-testing safety methods, and evals, with a focus on risks from scheming AIs. Examples include (black-box) AI control techniques, white-box monitors (probes etc.), chain-of-thought monitoring/faithfulness, building evaluation environments, and stress-testing mitigations.

The stream focuses on evaluating and/or mitigating catastrophic risk emerging from dangerous scientific capabilities in frontier AI systems, with an emphasis on the challenges that emerge from lab integrations and novel science. Potential research directions include evaluation design, risk mitigations and evaluation science.

I’m interested in two broad projects focused on improving current detection efforts at SecureBio. The first is to characterize when AI-bio or general AI tools are actually useful for large-scale metagenomic detection, including tradeoffs between compute cost, sequencing cost, model type, model size, and pipeline stage. The second is to explore genomic language models as novelty detectors—for example, using perplexity-style metrics to flag surprising sequences—and to evaluate whether this approach can complement traditional bioinformatics systems in a cost-effective, sensitive, and interpretable way.

Fourth Eon is developing adaptive, AI-native safeguards across the biotechnology stack, with a focus on function-based DNA synthesis screening. Fellows in this stream will work on technical research projects at the intersection of AI and biosecurity. Projects span topics like mechanistic interpretability of protein foundation models, bio model evaluations for biosecurity-relevant capabilities, and agentic sequence analysis workflows.

Backing projects focused on product development and organization building in the areas of AI safety and alignment, biosecurity, and critical cybersecurity. Looking for fellows who are self starters, default to action, and have a desire to create.

This stream will focus on projects related to biosecurity countermeasures.

This stream focuses on lead independent research in one of six chokepoints for biotech governance: live pathogen repositories, CROs, cloud labs, cell-free expression systems, plasmid vendors, or secondhand lab equipment. 

On high-conviction areas, you'll tackle specific open research questions and assess interventions; on low-conviction areas, you'll conduct deep dives to determine whether they're worth pursuing. Your findings will directly shape Sentinel's grantmaking strategy and provide strategic guidance to the broader biosecurity community.

Implementing SL4/5 and searching for differentially defense-favored security tools.

GDM stream focused on scheming risk, AI control, monitoring, monitorability, and loss-of-control evaluations. Probably running in-person in London.

Computational/modelling problems in biosecurity.