Research
Research Areas
Interpretability
Understanding how AI systems make decisions and what they learn from data.
Governance
Developing frameworks for responsible AI development and deployment.
Evaluations
Developing methods to assess AI capabilities, alignment, and safety properties.
Oversight / Control
Ensuring meaningful human oversight and control over AI systems.
AI Agency
Understanding and managing autonomous AI behavior and decision-making.
Security
Protecting AI systems from adversarial attacks and malicious use.
Recent Research by Durham AISI Members
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models
Leask, P., & Al Moubayed, N. (2025, July). Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models. Presented at International Conference on Machine Learning (ICML 2025), Vancouver, Canada
Sparse Autoencoders Do Not Find Canonical Units of Analysis
Leask, P., Bussmann, B., Pearce, M. T., Isaac Bloom, J., Tigges, C., Al Moubayed, N., Sharkey, L., & Nanda, N. (2025, April). Sparse Autoencoders Do Not Find Canonical Units of Analysis. Presented at The Thirteenth International Conference on Learning Representations, Singapore
Research Opportunities
Undergraduates
We can advise and support you on dissertation and individual study projects.
Faculty
We can signpost promising research directions and funding opportunities, and support you throughout.