Charlie Whittaker
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/.
Stream overview
I'd be excited to supervise projects that use epidemiological and mathematical modelling to explore and evaluate the comparative value, utility and cost-effectiveness of the wide range of interventions available across the preparedness and response spectrum: including early warning and detection systems, biohardening interventions (e.g. indoor air quality, PPE stockpiling), and medical countermeasures (broad-spectrum vaccines, therapeutics and diagnostics).
I'm particularly interested in how the relative value and prioritisation of these interventions changes under threat scenarios involving AI-enabled uplift to biological capabilities. Because such scenarios are deliberately engineered rather than naturally arising, they could differ substantially in their dynamics and context from the naturally occurring pathogens and zoonotic spillover that most preparedness modelling implicitly assumes, and I'd want a fellow to work through how and where those differences matter. The aim would be to build a clearer and more systematic sense of how preparedness priorities, and the balance of investment across these layers of defence, might need to shift or be reoriented in light of AI-enabled threats, rather than assuming that existing approaches (largely oriented around mitigating the risk and consequences of naturally occurring pathogens and zoonotic spillover) carry across unchanged.
I'm equally happy to supervise non-modelling, strategic and exploratory work in the same space. Projects in this vein might take the form of structured landscape analyses, theories of change for specific interventions, or carefully reasoned cases for particular preparedness strategies (grounded in the empirical and technical literature, but not necessarily requiring a formal model).
For a sense of the kind of work my group does more broadly, see here: https://whittakerlab.com/ and https://scholar.google.com/citations?hl=en&user=WGDceR8AAAAJ.
Mentors
Charlie Whittaker is an Assistant Professor in the School of Public Health at UC Berkeley, where he directs the Pandemic and Epidemic Threat Analytics Lab (PETAL). His research focuses on the dynamics, detectability and control of pathogens with pandemic potential, and uses computational modelling to explore how infectious diseases spread and to enhance preparedness and response strategies for public health emergencies and globally catastrophic biological risks. Current projects include work modelling the potential impact of broad-spectrum medical countermeasures during future pandemics, how to optimally structure and design next-generation surveillance systems and evaluation of indoor air disinfection technologies, amongst others.
Mentorship style
One 30-60 min weekly meeting by default. I'm pretty active on Slack and can usually respond to questions there within a day or two. I also anticipate you'll work closely with another member of my group (either a postdoctoral researcher or a PhD student, perhaps both), who will serve as another point of contact and also provide feedback/project assistance on an ~weekly basis.
Fellows we are looking for
Essential:
- Computational or technical research experience: Prior hands-on work in a computational (e.g. R, Python) or technical (e.g. mechanistic or statistical modelling) research setting.
- Relevant domain background: Expertise in biosecurity, computational biology, and/or infectious disease epidemiology, sufficient to engage critically with the substance of the problem being worked on.
- Critical thinking and autonomy: Strong critical thinking and creative problem-solving, paired with the autonomy to drive large portions of the work independently.
Preferred:
- Modelling exposure: Some prior experience with epidemiological, statistical, or mechanistic modelling.
- Decision-relevant framing: Familiarity with cost-effectiveness or decision analysis (or any background that involves turning quantitative work into prioritisation recommendations).
- Awareness of the AI–bio threat landscape: Some engagement with the discourse on AI-enabled biorisk and where the field thinks the key uncertainties lie.
Not a good fit:
- Fellows with no prior technical experience (much of our work involves math and computational programming).
Project selection
I'll provide fellows with a short list of potential projects and will meet with them to discuss which they feel most excited about / best suited for. Also very happy to chat through any specific ideas or proposals they might have and where they think I could be helpful!