Chris Holmes
Chris Holmes
Professor of Biostatistics in Genomics and Group Head / Principal Investigator
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.
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 postdoctoral position and then a lectureship at Imperial.
Prior 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 focused 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.
Recent publications
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To do no harm - and the most good - with AI in health care.
Journal article
Goldberg CB. et al, (2024), Nature medicine
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Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
Journal article
Coppock H. et al, (2024), Nature Machine Intelligence
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Where Medical Statistics Meets Artificial Intelligence. Reply.
Journal article
Hunter DJ. and Holmes C., (2023), N Engl J Med, 389, 2403 - 2404
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Authors’ reply to the Discussion of ‘Martingale Posterior Distributions’
Journal article
Fong E. et al, (2023), Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85, 1413 - 1416
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Martingale posterior distributions
Journal article
Fong E. et al, (2023), Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85, 1357 - 1391