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 2018
- Scheib C et al.
Ancient human parallel lineages within North America contributed to a coastal expansion.
Science, doi: 10.1126/science.aar6851 - Veeramah K et al.
Population genomic analysis of elongated skulls reveals extensive female-biased immigration in early medieval Bavaria.
PNAS, https://doi.org/10.1073/pnas.1719880115 - Kousathanas A et al.
A guide to general-purpose ABC software.
Handbook of Approximate Bayesian Computation, arXiv:1806.08320
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