Professor of Biostatistics
Fellow of Lincoln College
cholmes at stats.ox.ac.uk
+44 (0)1865 285874 (Direct)
+44 (0)1865 272865 (Department)
+44 (0)1865 272595 (Fax)
+44 (0)1865 285386 (PA - Madeline Mitchell)
Bayesian Statistics; Stochastic Simulation; Markov chain Monte Carlo; Pattern Recognition; Spatial Statistics; Statistical Genetics; Statistical Genomics; Genetic Epidemiology.
About my research
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.
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
Yau C, Papaspiliopoulos O, Roberts G and Holmes C (2011) Bayesian non-parametric hidden Markov models with applications in genomics. J Royal Stat Soc, Series B 73 (Part 1), 33-57
Lee A, Yau C, Giles M, Doucet A and Holmes C (2010) On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. J Comp & Graph Stats 19(4), 769-789
Wellcome Trust Case Consortium, Craddock N et al. (2010) Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature April 1, 464(7289), 713-720
Yau C, Mouradov D, Jorissen N, Colella S, Mirza G, Steers G, Harris A, Ragoussis J, Sieber O and Holmes C (2010) A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biol 11(9), (R12)
Lemieux J, Gomez-Escobar N, Feller A, Carret C, Amambua-Ngwa A, Pinches R, Day F, Kyes S, Conway D, Holmes C and Newbold C (2009) Statistical estimation of cell-cycle progression and lineage commitment in Plasmodium falciparum reveals a homogeneous pattern of transcription in ex vivo culture. Proceedings of the National Academy of Sciences, 106(18), 7559-7564
Giannoulatou E, Yau C, Colella S, Ragoussis J and Holmes C (2008) GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. Bioinformatics 24(19), 2209-2214
Jasra A, Stephens D and Holmes C (2007) Population-based Reversible Jump Markov Chain Monte Carlo. Biometrika 94(4) 787-807
Pintore, A., Speckman, P. and Holmes, C.C. (2006) Spatially adaptive smoothing splines. Biometrika , 96, 1, pp 113-125