Christopher C. Gill
Personal photo - Christopher C. Gill

Christopher C. Gill

Dept. of Statistics, St Giles, Oxford OX1 3QU

gill@stats.ox.ac.uk

ORCID iD iconorcid.org/0000-0001-5418-5483

I am fortunate to have joined the Marchini and Myers groups in statistical and population genetics in the Department of Statistics at the University of Oxford, working with the ancestry company LivingDNA. This is part of the Systems Approaches to Biomedical Sciences (SABS) doctoral program. I am currently interested in developing statistical tools and pipelines to provide new insights into genetic ancestry estimation.

Background

Having completed my degree in 2006, covering a wide range of mathematical topics, I went on to complete a D.Phil, focussing on the representation theory of finite groups. Much of my time was spent studying questions related to the decomposition numbers of the symmetric group. More specifically, I studied properties of Young modules, an important aspect of the representation theory of the symmetric group, and more generally trivial-source modules for finite groups. I spent several years pursuing research in this area, whilst enjoying a considerable amount of teaching. I held positions at Charles University in Prague, and at Somerville College, Keble College, and Hertford College in Oxford. In 2015 I decided to pursue a more interdisciplinary approach to research in the biological/biomedical setting. Through the intensive first year of the DTC, I completed a range of foundation courses covering several programming languages, biology, some applied mathematics, statistics, organic chemistry, and two three month rotation projects. The first was working with Prof. David Gavaghan in the Computational Biology Group, applying adaptive MCMC techniques to parameter fitting in numerical simulations of PDE models of electrochemical reactions. The second rotation was with Prof. Jonathan Marchini, developing a Bayesian technique for fitting a sparse parallel factor model of high dimensional datasets. The method was developed with gene expression data in mind, but is quite general and can be applied more widely.