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Computational Statistics & Machine Learning (OxCSML)

What we do

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.

Who we are

Academic staff

Research Staff

  • M. Azim Ansari
  • Yunlong Jiao
  • Luke Kelly
  • Gonzalo Mena (Florence Nightingale Fellow)
  • Chieh-Hsi (Jessie) Wu
  • George Nicholson
  • Tom Rainforth (Florence Nightingale Fellow)
  • Jun Yang (Florence Nightingale Fellow)


  • Fadhel Ayed
  • Christian Carmona Perez
  • Anthony Caterini
  • Sam Davenport
  • Giuseppe Di Benedetto
  • Emilien Dupont
  • Adam Foster
  • Adam Golinski
  • Bobby He
  • Robert Hu
  • Charline Le Lan
  • Zhu Li
  • Kaspar Märtens
  • Emile Mathieu
  • Cian Naik
  • Francesca Panero
  • Emilia Pompe
  • Dominic Richards
  • David Rindt
  • Matthew Willets
  • Jin Xu