Rohan Hitchcock

Rohan Hitchcock is a Researcher in the Department of Electrical and Electronic Engineering, focusing on interdisciplinary research at the intersection of machine learning and computational physics. His work emphasizes developing physically informed ML architectures to accelerate particle-based models, as reflected in his thesis titled 'Beyond the Black Box.' He is supervised by Prof Jonathan Manton.

His research interests span machine learning, computational physics, statistical mechanics, and algebraic topology. Recent articles highlight contributions to stochastic processes, neural network physics simulators, and theoretical frameworks in category theory.

Rohan's publications reflect a blend of applied and theoretical work, including studies on stochastic gradient Langevin dynamics (SGLD), entropy in thermodynamic systems, and advanced mathematical constructs like Landau-Ginzburg models and matrix factorizations.