MATS Autumn 2026

This stream focuses on mathematical modelling projects that quantify the comparative value and cost-effectiveness of pandemic and GCBR mitigating interventions (early warning and detection, biohardening, medical countermeasures etc), with a particular focus on how that picture shifts under threat scenarios involving AI-enabled uplift to biological capabilities, rather than a natural-emergence baseline. I'm also happy to supervise non-modelling, strategic and exploratory work in the same space (e.g. reasoning through how those AI-enabled scenarios actually differ and what they imply for the prospects of different interventions). For a better sense of what my team do more generally, have a look here: https://whittakerlab.com/.

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.

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.

Founding ambitious AI safety and field-building projects.

This is the empirical research stream of Eleos AI Research. We’re dedicated to understanding and addressing the potential wellbeing and moral status of AI systems. We are open to fellows working on a broad range of topics, including LLM introspection, LLM preferences, persona vectors, and more, using either white-box or black-box interpretability techniques. 

This stream offers 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.