In my work I explore how people solve problems using informal techniques like diagrams. I then computationally model this type of reasoning on computers to enable machines to reason in a similar way to humans. This involves:
developing knowledge representations that enable rigorous yet accessible reasoning in diverse domains;
devising techniques for automated reasoning systems to prove theorems using informal human-oriented approaches like diagrams, analogy and symmetry;
analysing and combining multiple representations (e.g., sentences, diagrams, images, natural language) in a uniform reasoning framework;
investigating the biological basis for human visual inference using neuroscience and machine learning;
applying these techniques to inference systems to better understand human reasoning.