Professor of Biostatistics
Fellow at St Anne's College
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.
- 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.
- Watson J, Nieto-Barajas L and Holmes C, Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence. Statistics 51 (3), pp. 558-571.
- Holmes C and S Walker, 2017, Assigning a value to a power likelihood in a general Bayesian model. Biometrika, 104 (2),pp. 497-403.
- de Angelis MH, Nicholson G, Selloum M, White J, Morgan H, Ramirez-Solis R, Sorg T, Wells S, Fuchs H, Fray M et al. 2015. Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics. Nat Genet, 47 (9), pp. 969-978.
- Taylor JC, Martin HC, Lise S, Broxholme J, Cazier JB, Rimmer A, Kanapin A, Lunter G, Fiddy S, Allan C et al. 2015. Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nat Genet, 47 (7), pp. 717-726.
- Pinnick KE, Nicholson G, Manolopoulos KN, McQuaid SE, Valet P, Frayn KN, Denton N, Min JL, Zondervan KT, Fleckner J et al. 2014. Distinct developmental profile of lower-body adipose tissue defines resistance against obesity-associated metabolic complications. Diabetes, 63 (11), pp. 3785-3797.
- Nicholson G, Rantalainen M, Li J, Maher A, Malmodin D, Ahmadi K, Faber J, Barrett A, Min J, Rayner N, Toft H, Krestyaninova M, Viksna J, Guha Neogi S, Dumas M-E, Sarkans U, MolPAGE Consortium, Donnelly P, Illig T, Adamski J, Suhre K, Allen M, Zondervan K, Spector T, Nicholson J, Lindon J, Baunsgaard D, Holmes, E, McCarthy and Holmes C (2011) A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet 7.