Originally trained in physics, I received my bachelors degree from the University of Cambridge and PhD in semiconductor quantum physics from Imperial College London. Subsequently I worked in radar signal and image processing R & D for nearly 10 years, but then converted to Life Sciences by doing a research masters in Bioinformatics at Newcastle University. I joined the Institute of Evolutionary Biology, University of Edinburgh, as a Computational Biologist to study Influenza and HIV in Professor Andrew Leigh Brown’s group 2007-2013, and Professor Andrew Rambaut’s group in 2010-2013. In 2013-2014 I worked in Professor Rowland Kao’s group in the Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow on molecular epidemiology in livestock. In October 2014 I joined the Roslin Institute, University of Edinburgh, as a Chancellor’s Fellow to develop computational techniques for utilising sequence data to investigate pathogen evolution and transmission patterns.  Now, since February 2019 I am a Group Leader in Pathogen Phylodynamics at the Roslin Institute, Infection and Immunity Division and have research programmes in viral and bacterial evolution and epidemiology.

Pathogen Phylodynamics - evolution and epidemiology of viruses and bacteria using bayesian and machine learning methods.

I am interested in the evolution and epidemiology of viruses and bacteria, and my research makes use of the large quantity of pathogen sequence data now available. Pathogen sequences accumulate mutations over time, and this information can be used to infer transmission patterns. I apply machine learning techniques and Bayesian phylogenetics to investigate cross species transmissions, host adaptations, epistatic interactions, phylodynamics and phylogeography. I'm currently developing fast computational methods and simulation tools to infer transmission patterns of livestock pathogens including Avian and Swine Influenza, Bovine TB and Bovine Viral Diarrhoea.

Viruses and bacteria have DNA (or in some cases RNA) genomes which mutate rapidly. By tracking where, when and in what species particular mutations or genomic re-arrangements arise, it is possible to infer transmission patterns between groups of individuals, e.g. patterns of farm to farm transmissions for fast mutating livestock pathogens; global transmission patterns between countries (mediated by trade or travel patterns); or transmission between wild and farmed animals. Genomic sequencing is now relatively inexpensive and reasonably fast and there is a large amount of pathogen sequence data available - the challenge is to develop analysis and prediction methods. I am researching fast but robust computational bayesian and machine learning methods for transmission pattern inference, integrating sequence and other epidemiological data, for example considering avian and swine influenza, bovine tuberculous and bovine viral diarrhoea. I aim to be able to answer questions such as - what are the routes and rates of infection ? what are the rates of cross species transmissions ? and how quickly might new strains arise ?

Current students:

• Florian Duchatel
• Jordan Ashworth
• Kajetan Stanski
• Heather Grant
• Rachel Bragg
• Oumie Kuyateh
• Antonia Hilbig
• Thomas Dalhuisen

Former students:

• Andrew Mason
• Lu Lu
• Manon Ragonnet
• Mojca Zelnikar
• Melissa Ward