Chris Holmes
Chris Holmes
Professors 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 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.
Recent publications
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Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.
Journal article
Willetts M. et al, (2022), Scientific reports, 12
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Building an evidence standards framework for artificial intelligence-enabled digital health technologies
Journal article
Unsworth H. et al, (2022), The Lancet Digital Health, 4, e216 - e217
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Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: A population-based ecological study.
Journal article
Padellini T. et al, (2022), The Lancet regional health. Europe
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Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag
Journal article
Jersakova R. et al, (2022), Journal of the Royal Statistical Society. Series C: Applied Statistics
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Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework.
Journal article
Nicholson G. et al, (2022), Nat Microbiol, 7, 97 - 107