Professor Chris Holmes

Professor of Biostatistics

Biographical Sketch

I moved to Oxford from Imperial College London in February 2004. At Imperial College I studied for my doctorate in Bayesian statistics, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Previous to this I worked in industry for a number of years researching in scientific computing, developing techniques for real-time pattern recognition models in defence and SCADA (Supervisory Control and Data Acquisition) systems. My current research is focussed on applications and statistical methods development in the genomic sciences and genetic epidemiology. I hold a Programme Leaders Grant in Statistical Genomics from the Medical Research Council.

Research Interests

  • Bayesian statistics
  • Stochastic simulation
  • Markov chain Monte Carlo
  • Pattern recognition
  • Spatial statistics
  • Statistical genetics
  • Statistical genomics
  • Genetic epidemiology

I have a broad interest in the theory, methods and applications of statistics and statistical modelling. My background and beliefs lie in Bayesian statistics which provides a unified framework to stochastic modelling and information processing. I am particularly interested in pattern recognition and nonlinear, nonparametric methods.


Ter-Minassian, L., Clivio, O., Diaz-Ordaz, K., Evans, R. and Holmes, C. (2023) “PWSHAP: a path-wise explanation model for targeted variables”, in Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, pp. 34054–34089.
Falck, F., Williams, C., Danks, D., Deligiannidis, G., Yau, C., Holmes, C., Willetts, M. and Doucet, A. (2023) “A multi-resolution framework for U-nets with applications to hierarchical VAEs”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 15529–15544.

Contact Details

College Affiliation: Fellow at St Anne's College


Telephone: +44(0)1865 285874

Office number: 1.08