I am a mathematician working across category theory, homotopy theory, type theory, algebraic geometry, probability and machine learning.
As an applied mathematician and machine-learning researcher, I’m interested in:
(1) the application to deep learning of functorial semantics, algebraic geometry, and statistical learning theory; and
(2) the application of deep learning to automated theorem proving.
As a pure mathematician, I am interested in synthetic treatments of varying domains of mathematics—viz. stable homotopy theory, probability theory, and the theory of ∞-categories—developing type systems for such and leveraging the new understandings and sensibilities made possible by those treatments to reimagine foundational questions.
From August 2023 to October 2025 I served as Senior, then Principal, Scientist with Symbolica.
The researchers at Symbolica, lead by John Wilmes, Bruno Gavranovic, and I put together the two-part program which won Symbolica USD 31 million in our Series A (https://venturebeat.com/ai/move-over-deep-learning-symbolicas-structured-approach-could-transform-ai).
Before Symbolica, I was a postdoctoral researcher in Category Theory at the Centre of Australian Category Theory at Macquarie university.