My central interest is in applying machine learning tools to solve logical problems, making use of principals from geometric deep learning to utilise logical symmetries. My hope is that by making AI more logical we can create more noise-resistant, explainable and trustworthy systems.

My background is in Logic and Computation, with recent research projects spanning topics such as Automata Theory, Quantum Computing, Computational Social Choice, Cognitive Modelling, Logical Programming, Philosophy of AI and Proof Theory. As a researcher and developer at Fermioniq (2022-2024), I have also worked on improving the capacities of classical quantum circuit emulators using tensor network techniques.

I love teaching and presenting, and I’m always looking for ways to apply my technical background to social issues. Currently I’m helping GOAL3; using AI models with Logical structure to predict critical illness events in low-resource children ICUs, building towards an explainable (!) and trustworthy (!) prediction tool that can help save lives.

If you are interested in any of these topics and would like to discuss ideas, feel free to send me a message on Linkedin. I am always open to talk.