My research interests are broad but mostly centre around the combination of statistical and symbolic AI, and in particular how this can be used to improve robustness, explainability, and safety. Currently these interests are focussed on developing techniques to help rigourously identify or induce particular properties of multi-agent systems under their game-theoretic equilibria, especially those systems that operate in uncertain (partially known, partially observable, stochastic, etc.) environments. Related topics that I have worked on before include statistical relational learning, formal verification of learnt models, and deep symbolic reinforcement learning. Much of my work is also concerned with the problem of how agents represent and reason about preferences in the face of uncertainty. Examples of this include projects on representing non-Markovian reward structures using automata, learning models of cognitive biases using inverse reinforcement learning, and my master's dissertation on computational frameworks for moral decision-making. Thirdly and finally, I also take an interest in the governance, ethics, and societal impact of AI, though these are not topics I am actively researching at present.
I am supervised by Michael Wooldridge, Alessandro Abate, and Julian Gutierrez, and am also a DPhil Affiliate at the Future of Humanity Institute. Before coming to Oxford I worked as an intern on Imandra and was a research assistant for Jacques Fleuriot at the University of Edinburgh, where I completed my MSc in artificial intelligence under the supervision of Vaishak Belle. Prior to this I studied for my BSc in mathematics and philosophy at the University of Warwick with Walter Dean.
- Future of Humanity InstituteDPhil Affiliate
- Probabilistic Model Checking
- Machine Learning Systems
- Computational game theory
- Logics for strategic reasoning
- Complexity and equilibria
- Quantitative Analysis and Verification
- Rational verification