I am a mathematical machine learning researcher with engineering experience in alternative data pipelines and modular execution frameworks. I hold a PhD from Goldsmiths, University of London, where I developed computational models of music cognition.
My research focuses on machine learning algorithms with algebraic, geometric, or probabilistic constraints. I led an algebraic machine learning research programme that extended robust PCA to cyclic group algebras (TSP, 2016), matrix completion to abelian group algebras (CIKM, 2017), and deep learning to arbitrary algebras (TNNLS, 2020). More recently I developed a model-based PCA method with a user-extensible library of error covariance structures (CSDA, 2025).
In my spare time, I enjoy playing piano and chess.