My research focuses on developing the next generation of AI systems that can use explanation as a core mechanism for learning and reasoning, particularly in complex domains such as science, mathematics, and healthcare.

I investigate the integration of neural and symbolic AI methods to enhance the robustness and faithfulness of AI-generated explanations and, ultimately, to understand the principles governing explanatory inference in humans.

Moreover, I am interested in developing methodologies to interpret, control, and evaluate Large Language Models (LLMs), with a focus on disentangling knowledge acquisition from abstract logical reasoning, and enabling out-of-distribution, out-of-domain generalisation.

I am building a research lab with a core focus on explanation-based learning and reasoning.

If you're interested in pursuing a PhD or discussing potential opportunities for collaborations, feel free to reach out!