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.

Publications

Ghalebikesabi, S., Ter-Minassian, L., Diaz-Ordaz, K. and Holmes, C. (2021) “On Locality of Local Explanation Models”, in ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021).

Fong, E. and Holmes, C. (2021) “Conformal Bayesian Computation”, in ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021).

Camuto, A., Wang, X., Zhu, L., Holmes, C., Gurbuzbalaban, M. and Simsekli, U. (2021) “Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections”, in INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139.

Lehmann, B., Mackintosh, M., McVean, G. and Holmes, C. (2021) “Optimal strategies for learning multi-ancestry polygenic scores vary across traits.”

Zaidi, S., Zela, A., Elsken, T., Holmes, C., Hutter, F. and Teh, Y. (2021) “Neural Ensemble Search for Uncertainty Estimation and Dataset Shift”, in Advances in Neural Information Processing Systems, pp. 7898–7911.

Camuto, A., Willetts, M., Paige, B., Holmes, C. and Roberts, S. (2021) “Learning Bijective Feature Maps for Linear ICA”, in Proceedings of Machine Learning Research, pp. 3655–3663.

Willetts, M., Camuto, A., Rainforth, T., Roberts, S. and Holmes, C. (2021) “IMPROVING VAES’ ROBUSTNESS TO ADVERSARIAL ATTACK”, in ICLR 2021 - 9th International Conference on Learning Representations.

Wilde, H., Jewson, J., Vollmer, S. and Holmes, C. (2021) “Foundations of Bayesian Learning from Synthetic Data”, in 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS), p. 541 - +.

Willetts, M., Roberts, S. and Holmes, C. (2020) “Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 5286–5295.

Kendall, M., Milsom, L., Abeler-Dörner, L., Wymant, C., Ferretti, L., Briers, M., Holmes, C., Bonsall, D., Abeler, J. and Fraser, C. (2020) “Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis”, The Lancet Digital Health, 2(12), pp. e658 - e666.