I am a biostatistician and evolutionary biologist.  Prior to taking a position as a consultant, I was a researcher in the Department of Statistics at the University of Oxford as a member of Prof David Steinsaltz's group in biodemography. I am an expert in quantitative biology with extensive experience in teaching and training. My expertise ranges from experimental design to machine learning, with specialised knowledge in data handling of large longitudinal data. My background as a researcher has given me the experience necessary to support grant applications and manuscript writing, as well as ensure that good statistical practices are embedded in research projects. I am particularly passionate about developing and delivering training in statistics and R for non-statisticians.

2022- present: Senior Statistical Consultant, Oxford University Statistical Consulting

2018-2022: Postdoctoral Researcher in Biostatistics, University of Oxford

2016-2018: Postdoctoral Researcher in Sociogenomics, University of Oxford

2012-2016: PhD in Biological Sciences, University of Reading

2009-2010: MSc Plant Diversity - Taxonomy and Evolution, University of Reading

2006-2009: BSc(Hons) Biological Sciences, Imperial College London

2003-2006: BSc(Hons) Mathematics with Statistics, Imperial College London

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.

I lived in Denmark until 1985, except for short periods in England, Austria and Italy. Then I had a very long series of postdoctoral positions in North Carolina, California, Montreal, Japan and a few other places. From 1991 to 2001 I was a lecturer at Aarhus University and in 2001 I moved to Oxford, where I have been ever since, except for sabbaticals in New Zealand, Berkeley and Chapel Hill.

Stochastic and Algorithmic Aspects of Molecular Evolution, Origins of Life and Population Genetics.

Most of my interests involve molecular evolution and molecular population genetics.

Statistical Alignment. A major project in the last 5-7 years has been the development of algorithms calculating the likelihood of a set of homologous sequences that has evolved by both insertions, deletions and substitutions. This field is also called Statistical Alignment since it can produce alignments and probability statements about the alignment. The motivation for studying, modelling and making algorithms for statistical alignment is that most sequence analysis has, in the last decade or more, benefited tremendously from the use of stochastic models of sequence evolution. This also allows parameter estimation, hypothesis testing and more.

Ancestral Recombination Graph. Colleagues and I have developed a method that will find a history of a set of sequences that minimizes the number of recombinations plus substitutions and displays a history of the sequences that use this minimal number of events. This history will also find a set of intervals within which there hasn’t been any recombinations. This is a rational definition of a haplotype block. It is highly computationally demanding.

Evolutionary Models. This is a large topic and anything where you can have two examples and call them homologous in principle needs an evolutionary model. I have been interested in networks, but presently multigene families and protein structures are the focus. Multigene families have been studied for decades but only recently have they been viewed as a statistical problem.

Computational Models of Origins of Life. This is one of science’s great unsolved problems that is bound to get much more attention in the near future and turn much more computational as with everything else in the biosciences.

I studied Mathematics (MMath) at the University of Warwick and Financial Mathematics (MSc) at Heriot-Watt University and the University of Edinburgh. After obtaining my PhD from the School of Mathematical Sciences of the University of Nottingham under the supervision of Sergey Utev and Huiling Le, I moved to the Department of Mathematics of the University of Leicester as a Teaching Assistant/Fellow. In 2012 I moved to the Department of Statistics of the University of Oxford as Departmental Lecturer. I stayed in Oxford until September 2016 when I moved to the Department of Mathematics of King’s College London as Lecturer in Statistics. I moved back to the University of Oxford in December 2017 as Associate Professor of Statistics. I was promoted to Professor of Statistics in August 2024. 

I work in the intersection of probability and statistics to analyse random processes and objects, especially those arising from algorithms used in computational statistics and machine learning. I have worked extensively on the theory and methodology of sampling methods, especially Markov Chain Monte Carlo. I have also worked on random walks on lattices and groups.
At the moment I am particularly interested in the interplay between sampling, optimization and machine learning.

I was educated in France where I graduated jointly from ENSAE (National School for Statistics and Economic Administration) and Université Paris VI in 2000. After my PhD in Paris VI (2003) in Probability, I was Maitre de Conférences in Marseille for three years and then in Paris until 2014 when I joined the Department of Statistics and Magdalen College in Oxford.

During this time I have also twice been visiting professor at NYU-Abu Dhabi as well as a long term visitor at the University of Bath.

• Branching processes
• Branching random walks
• Coalescence
• Fragmentation
• Population genetics
• Reaction-diffusion equations
• Front propigation
• Random trees

My research is in probability theory and focuses essentially on models and situations which involve tree-like structures and branching phenomena. Examples include coalescent processes, branching processes, continuous random trees, branching random walks… These models are not only endowed with a remarkably rich mathematical structure that connects them to many area of mathematics, but they also occur naturally in physical sciences, in population genetics and in biology. Questions that arise in these fields are a major motivation of my work.