°µÍø½ûÇø

Paul is an incoming assistant professor in computer science at the °µÍø½ûÇø (Fall 2026). His research explores the fundamentals of symbolic learning and synthesis, e.g. learning programs or logic formulas, with particular interest in the question of how to learn and synthesize symbolic abstractions and languages for new domains. He has worked on applications of logic formula learning to mathematical discovery as well as visual discrimination within neuro-symbolic systems, and is interested in further applications to the design, optimization, and verification of software across the computing stack. A long-term goal is to develop design principles for building computer systems out of learned components while retaining an ability to reason about these same systems and their compositions. While his expertise falls within formal methods, logic, and programming languages, he is eager to collaborate across disciplinary boundaries and is particularly interested in using symbolic learning to support human creativity in areas like math and music. Paul has a PhD in computer science from the University of Illinois where he was advised by Madhusudan Parthasarathy.