What we do
When observing nature, it is easy to be impressed by the huge diversity seen on any biological scale. Our primary aim is to better understand the underlying evolutionary and ecological processes that have been shaping this diversity over the course of evolution on our planet. To achieve this, we design and evaluate new statistical and computational approaches to infer complex evolutionary histories. For this, we develop and apply machine learning algorithms, with a particular focus on likelihood-free methods. We then apply these approaches to the wealth of data currently being generated.
Main publications 2017
- Gros-Balthazard M et al. The discovery of wild date palms in Oman reveals a complex domestication history involving centers in the Middle East and Africa. Current Biology 2017 27: 2211-2218.e.8.
- Kousathanas A et al. Inferring heterozygosity from ancient and low coverage genomes. Genetics 2017 205: 317-332.
- Duchen P et al. Inference of evolutionary jumps in large phylogenies using Lévy processes. Systematic Biology 2017 syx028.
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