Computational Statistics and Machine Learning

Computational Statistics and Machine Learning

The Computational Statistics and Machine Learning Group is led by Professor Mark Girolami and develops fundamental algorithms for statistical inference in complex systems, with applications ranging from complex fluid flows to high-dimensional traffic simulations.

Core Research Topics
• Geometric sampling methods for complex probability measures.
• Bayesian methods for (nonlinear) differential equations.
• Probabilistic machine learning & generative modelling.
• Decision making under uncertainty, data-centric engineering.