Simon Tavare

Columbia University


Primary Section: 32, Applied Mathematical Sciences
Secondary Section: 29, Biophysics and Computational Biology
Membership Type: International Member (elected 2018)

Biosketch

Simon Tavaré is a statistician recognized for his work at the interface between the mathematical sciences and the biological and medical sciences. He is known particularly for probabilistic and statistical aspects of coalescent theory, evolutionary approaches to cancer, computational biology and approximate Bayesian computation. Born in Australia, he grew up in England. He obtained his PhD in Probability and Statistics from the University of Sheffield, and began his research career in the USA in 1978. After positions at the University of Utah, Colorado State University and the University of Southern California, where he held the George and Louise Kawamoto Chair in Biological Sciences, he moved in 2003 to the University of Cambridge as Professor of Cancer Research in the Department of Oncology, a group leader in the Cancer Research UK Cambridge Institute and a Professor in the Department of Applied Mathematics and Theoretical Physics. From 2013 to 2018 he was director of the Cambridge Institute. He is a fellow of the Academy of Medical Sciences and the Royal Society, and a member of EMBO. He was president of the London  Mathematical Society from 2015 to 2017.

Research Interests

Simon Tavaré’s early research focused on statistical and stochastic  aspects of population and evolutionary genetics, statistical inference in molecular biology, human genetics, molecular evolution and paleontology, stochastic computation and probabilistic combinatorics. His research group currently focuses on statistical bioinformatics and computational biology.  They have developed methods for the analysis of bead-based microarrays and sequencing experiments, and have significant collaborations  in the areas of glioblastoma and esophageal adenocarcinoma. A major focus has been the development of statistical and stochastic methods for understanding tumor heterogeneity and evolution by combining methods from population genetics with somatic replication. These methods are now being developed to  understand the interaction among cell types  in solid tumors by exploiting  technologies for molecularly annotating single cells in spatially resolved detail.

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