Alan Cooney
This stream focuses on empirical AI control research, including defending against AI-driven data poisoning, evaluating and attacking chain-of-thought monitorability, and related monitoring/red-teaming projects. It is well-suited to applicants already interested in AI safety with solid Python skills, and ideally prior research or familiarity with control literature/tools (e.g. Inspect/ControlArena).
Stream overview
- Control for data poisoning. Studying scenarios where an AI system covertly data poisons another AI, to e.g., instill backdoors or secret loyalties.
- Chain-of-thought monitorability. Evaluating the monitorability of reasoning verbalised by AIs & studying ways this may be compromised.
- Other Control projects (see https://alignmentproject.aisi.gov.uk/research-area/empirical-investigations-into-ai-monitoring-and-red-teaming for ideas)
Mentors
Alan is Head of Autonomous Systems & Control at the UK AI Security Institute, where he works on empirical AI control and monitoring. He co-authored RepliBench, an evaluation suite measuring autonomous-replication capabilities in language-model agents.
Mentorship style
1-hour weekly meetings for going through your research log & high level guidance. Daily updates on slack are also very useful and I typically reply within 2 days to any questions.
Fellows we are looking for
Essential:
- Existing interest in AI safety
- Programming experience with Python/similar
You may be a good fit if you also have some of:
- Research experience: prior AI research experience (on any topic)
- Strong Python skills (essential for CoT monitorability eval projects)
- Familiarity with the Control literature - see here
- Familiarity with Inspect/ControlArena
Not a good fit:
- Scholars primarily interested in conceptual rather than empirical research
Collaborating with other MATS scholars.
Project selection
By default I'll propose several projects for you to choose from, but you can also pitch ideas that you're interested in.
The Winter 2027 cohort offers a wide range of research streams led by experts across AI alignment, interpretability, governance, and safety. Each stream provides its own research agenda, methodology, and mentorship focus.
