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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Optimal strategies for learning multi-ancestry polygenic scores vary across traits.
Journal article
Lehmann B. et al, (2023), Nature communications, 14
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Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database.
Journal article
Sinha S. et al, (2023), European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery, 63
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Statistical inference with exchangeability and martingales.
Journal article
Holmes CC. and Walker SG., (2023), Philos Trans A Math Phys Eng Sci, 381
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Generating the right evidence at the right time: Principles of a new class of flexible augmented clinical trial designs.
Journal article
Dunger-Baldauf C. et al, (2023), Clin Pharmacol Ther
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A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic.
Journal article
Li G. et al, (2023), Environ Int, 172