Damon Binder (Biosecurity stream)
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.
Stream overview
I am excited to supervise projects related to the following:
- Evaluating specific AI-bio policy levers. For example: How much risk reduction do we get from narrow dataset restrictions on frontier models — and is this a foreseeably losing strategy? How far behind frontier are open-weight models going to be on bio-relevant capabilities, and how does that gap evolve?
- Capability forecasting for bio and adjacent domains. When does AI trivialise specific offensive or defensive capabilities — in bio (designing novel pathogens, detecting engineered organisms, developing medical countermeasures), but also in adjacent areas relevant to catastrophic risk (cyber, nuclear, persuasion)? What are the key technical bottlenecks, and how fast are they falling?
Mentors
I am a researcher on the Biosecurity and Pandemic Preparedness team at Coefficient Giving, where I investigate risks from biology, and also think about whether AI could enable other dangerous technologies. Prior to this I worked at the Future of Humanity Institute, and before that completed a PhD in physics at Princeton.
Mentorship style
Half-hour one-on-one weekly meetings by default, with the option to extend or add ad-hoc calls when useful. I'm active on Slack and typically respond within a day for quick questions. I'm happy to read drafts and leave written feedback async between meetings.
Fellows we are looking for
Essential:
- Eagerness to use and experiment with AI tools in novel ways.
- Research independence.
- Intellectual breadth and curiosity. Someone excited to work across many domains of science and to learn a lot in the process.
Preferred:
- Strong quantitative background.
- Background in a natural science or engineering.
- Prior experience driving a research project to completion.
- Some coding experience, ideally in Python.
- Comfort doing fast back-of-envelope modelling under uncertainty.
Project selection
I'll talk with the fellow about what they're interested in, and we'll pick a broad area together from a few directions I'd want to pitch. From there we'll work together to scope something sharp and well-defined, with me leaning on my sense of what's tractable and high-value. The fellow then runs with the project, and we adjust as it develops.