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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Principles of Experimental Design for Big Data Analysis.
Journal article
Drovandi CC. et al, (2017), Stat Sci, 32, 385 - 404
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Assigning a value to a power likelihood in a general Bayesian model
Journal article
Holmes CC. and Walker SG., (2017), Biometrika, 104, 497 - 503
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Encrypted accelerated least squares regression
Journal article
Esperança PM. et al, (2017), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 334 - 343
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The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq.
Journal article
Wills QF. et al, (2017), BMC genomics, 18, 53 - 53
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A note on statistical repeatability and study design for high-throughput assays.
Journal article
Nicholson G. and Holmes C., (2016), Stat Med