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

Goldberg, C., Adams, L., Blumenthal, D., Brennan, P., Brown, N., Butte, A., Cheatham, M., deBronkart, D., Dixon, J., Drazen, J., Evans, B., Hoffman, S., Holmes, C., Lee, P., Manrai, A., Omenn, G., Perlin, J., Ramoni, R., Sapiro, G., Sarkar, R., Sood, H., Vayena, E., Kohane, I. and RAISE Consortium, . (2024) “To do no harm - and the most good - with AI in health care.”, Nature medicine, 30(3), pp. 623–627.

Fong, E., Holmes, C. and Walker, S. (2024) “Authors’ reply to the Discussion of ‘Martingale Posterior Distributions’”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 85(5), pp. 1413–1416.

Fong, E., Holmes, C. and Walker, S. (2024) “Martingale posterior distributions”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 85(5), pp. 1357–1391.

Coppock, H., Nicholson, G., Kiskin, I., Koutra, V., Baker, K., Budd, J., Payne, R., Karoune, E., Hurley, D., Titcomb, A., Egglestone, S., Tendero Cañadas, A., Butler, L., Jersakova, R., Mellor, J., Patel, S., Thornley, T., Diggle, P., Richardson, S., Packham, J., Schuller, B., Pigoli, D., Gilmour, S., Roberts, S. and Holmes, C. (2024) “Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers”, Nature Machine Intelligence, 6(2), pp. 229–242.

Menacher, A., Nichols, T., Holmes, C. and Ganjgahi, H. (2024) “Bayesian lesion estimation with a structured spike-and-slab prior ”, Journal of the American Statistical Association [Preprint].

Hunter, D. and Holmes, C. (2023) “Where Medical Statistics Meets Artificial Intelligence. Reply.”, The New England journal of medicine, 389(25), pp. 2403–2404.

Hunter, D. and Holmes, C. (2023) “Where Medical Statistics Meets Artificial Intelligence.”, The New England journal of medicine, 389(13), pp. 1211–1219.

Whitehead, M., Carrol, E., Kee, F. and Holmes, C. (2023) “Making the invisible visible: what can we do about biased AI in medical devices?”, BMJ (Clinical research ed.), 382, p. 1893.

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.

Lehmann, B., Mackintosh, M., McVean, G. and Holmes, C. (2023) “Optimal strategies for learning multi-ancestry polygenic scores vary across traits.”, Nature communications, 14(1), p. 4023.