Projects

This project assesses the capabilities of AI-enabled and non-AI biological tools relevant to hazardous-virus design and examines their implications for biosecurity risk.

This project automates viral threat detection by screening viral genomes for high-risk recombination events. It applies a sliding-window algorithm across sequence junctions in NCBI and CASPER metagenomic data, producing a quantitative “surprise score” that automatically flags novel, anomalous viral variants for early biosurveillance triage.

This project revises and expands Coefficient Giving’s practical guide to household protection during extreme biological events, covering methods for reducing exposure through air, food, water, surfaces and other pathways. It assesses existing recommendations, identifies evidence gaps and develops clearer guidance for maintaining protection throughout prolonged emergencies.

This project, conducted with Sebastian Oehm and Askar Kleefeldt at SynX Therapeutics, maps global macrocyclic-peptide manufacturing capacity and its supply-chain vulnerabilities. It tests whether rapid-response therapeutics could be produced at population scale during a severe pandemic or deliberate attack, including threats enabled by AI-driven biological design.

This project ranks candidate experiments by cost, timeline and decision-relevance, and establishes the funding decision rules to pre-commit to before results arrive.

This project assesses whether genome language models can identify viruses that are increasingly different from those in their training data, informing both detection efforts and assessments of the models’ own capabilities.

This project conducts early engineering and strategic work toward the first draft of a handbook detailing how the average person can maximise their chances of surviving an extreme environment-to-human biological disaster, such as a mirror-bacteria pandemic.

This project investigates mirror bacteria, which could plausibly evade immune and ecological control if released. It reproduces recent population-model analyses suggesting that they would struggle to establish, then tests how strongly those conclusions depend on assumptions about predation, nutrient dynamics and spatial structure. This sharpens the quantitative basis for assessing mirror-life risk.

This project creates the Observatory, a public, source-linked dataset of frontier AI companies’ biological commitments. It records what is specific to biological risk beneath a generic “CBRN” label, who can authorise crossing a threshold, and whether each claim is binding or aspirational.

This project examines the gap between the theoretical effectiveness of pandemic PPE and its performance in real-world use, from a human-factors perspective.

This project tests whether predicted viral protein structures can replace experimentally derived structures when training inverse protein-folding models, gauging the potential effectiveness of pathogen data controls.

This project examines indoor air quality (IAQ) as a way to reduce airborne infectious-disease transmission, given that the vast majority of respiratory infections occur indoors. It assesses which interventions should be deployed in different contexts and investigates the policy levers needed to make the air in public spaces safer to breathe.

This project identifies current obstacles to developing mirror life and assesses the risk that AI could accelerate research to overcome them.

This project addresses the lack of an established route for concerning biological capability findings from frontier AI pre-deployment evaluations to reach the UK government. Drawing on evaluator interviews and the cyber precedent of coordinated vulnerability disclosure, it develops technical guidance for labs and evaluators on what to share, alongside recommendations for UK government bodies on how to receive it.

This project improves estimates of how sensitive wastewater sequencing is at detecting respiratory viruses by pairing untargeted nasal-swab prevalence data with deep wastewater metagenomics. It will help public health agencies price and size biosurveillance programmes against a defensible detection threshold rather than a guess.