Eric received a BA in Natural Sciences (specialising in Zoology) from Cambridge in 2003.
He started a PhD on the evolution of sociality in wasps at UCL in 2005, moving to the University of Sussex in 2007 and obtaining his PhD in 2009. After this,Eric moved to the University of Lausanne, Switzerland, for a PostDoc investigating the transcriptional basis of ageing and lifespan in ants.
He came to LSTM in 2016 to study the evolutionary genomics of insecticide resistance in mosquitoes Eric's work on insecticide resistance focuses on two main aspects. The first aims to understand the importance of gene duplication, and its association with insecticide resistance. Genome-wide analysis have shown that duplications have played an important role in the evolution of resistance, with repeated independent mutations in the same set of genes driven to high frequencies by positive selection. The second aspect aims to build predictive models of insecticide resistance using genetic and genomic data, in particular through the use of machine learning models and whole-genome sequencing data.
Eric is part of the analysis team for the Anopheles gambiae 1000 Genomes (Ag1000G) project and the Vector Observatory. Eric is also a member of the Genomics for African Anopheles Resistance Diagnostics (GAARD) network, which bring together researchers in the UK (LSTM, Lancaster, Oxford) and five Sub-Saharan African countries (Centre Suisse de Recherches Scientifiques, Côte d'Ivoire; University of Cape Coast, Ghana; University of Abomey-Calavi, Benin; National Institute for Medical Research, Tanzania; Kenya Medical Research Institute, Kenya), aiming to gain a longitudinal understanding of the evolution of insecticide resistance across a broad geographic range. Applying his research on ageing to disease vectors,
Eric is also developing transcriptomic markers of age in the tsetse fly Glossina morsitans, with the aim of applying these to field collections to determine the age structure of natural populations. Estimating and tracking the population age distribution is important for understanding the dynamics of vector-borne transmission and for accurately monitoring the impact of vector control interventions.