Computational Statistics & Machine Learning (OxCSML)

The members of the Computational Statistics and Machine Learning Group (OxCSML) have research interests spanning Statistical Machine Learning, Monte Carlo Methods and Computational Statistics, Statistical Methodology and Applied Statistics

Research in Statistical Machine Learning spans Bayesian probabilistic and optimization based learning of graphical models, nonparametric models and deep neural networks, and complements research in Monte Carlo methods for related classes of problems. Researchers in Statistical Methods develop very general statistical methodology.

Research in Applied Statistics motivates the more theoretical work in this group and some staff focus on developing statistical methodology ‘on demand’ in a wide range of application domains.

Academic staff

• François Caron
• Arnaud Doucet
• Robin Evans
• Chris Holmes
• Geoff Nicholls
• Patrick Rebeschini
• Jennifer Rogers
• Judith Rousseau
• Dino Sejdinovic
• Yee Whye Teh

Research Staff

• Angelos Armen
• Marco Battiston
• Benjamin Bloem-Reddy
• Luke Kelly
• Tom Rainforth
• Chieh-Hsi Wu