Simon Tavaré Program in Molecular and Computational Biology Department of Biological Sciences University of Southern California 1050 Childs Way, RRI 201B Los Angeles, CA 90089-2910 Phone: 213 740 8766 FAX: 213 740 2437 Email: stavare at usc dot edu

Biographical Sketch

I have been at USC since 1989, where I am now a Research Professor and hold the George and Louise Kawamoto Chair in Biological Sciences. My research has focused on statistical problems arising in molecular biology, human genetics, population genetics, molecular evolution and bioinformatics.

I am an associate editor of Annals of Applied Statistics, Annals of Human Genetics, Journal of Computational Biology and Statistics Surveys, on the Editorial Board of CaPR (Cancer Prevention Research), MathematicS In Action, Human Genomics, J R Soc Interface, and Evolutionary Bioinformatics Online. and past Associate Editor for 10 other journals in probability, statistics and biology.

Since 2003, I have been a professor in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge, UK. Further details may be found here. I am also Professor of Cancer Research (Bioinformatics) in the Oncology Department and Senior Group Leader in the new Cancer Research UK Cambridge Research Institute (CRI). There our research focusses on cancer genomics. For further details click here.

• Physics of Medicine Initiative, University of Cambridge.
• Gene Ontology Consortium
• BioConductor Project
• Cornell Research Training Group in Probability
• PUMPKIN (Membrane pumps in cells and disease), University of Aarhus
• Gothenburg Mathematical Modelling Centre (GMMC)
• Institute for Pure and Applied Mathematics (IPAM)
• Allan Wilson Center for Molecular Ecology and Evolution
• Cambridge Computational Biology Institute (CCBI), University of Cambridge
• Eurandom [Statistical Information and Modelling program]

CV and Publications

• Downloadable Publications [pdf format]
• Complete CV [May 20, 2008]

Current Research Interests in the Group

• Stochastic computation (MCMC without likelihoods, approximate Bayesian computation)
• Population genetics (inference for coalescent processes, linkage disequilibrium mapping and the HapMap Project
• Stem Cells (methods for using methylation patterns to study stem cell evolution and mitotic division)
• Statistical genomics (microarrays, including expression studies in D. melanogaster, C. elegans, genome evolution and motif detection in E. coli, origins of replication in S. cerevisiae.)
• Tracking algorithms (particularly for following behavior of multiple flies in real time)
• Probabilistic combinatorics

Current Research Funding

We are supported by grants from the National Institutes of Health.