Scientists behind a computer looking at digital DNA strands

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Matteo Fumagalli profile photo

Matteo Fumagalli

Dr Matteo Fumagalli is a biomedical engineer by training. He completed his PhD at Politecnico di Milano (Italy) and then undertook a postdoctoral research position at University of California – Berkeley funded by EMBO. He then moved as a research fellow at UCL funded by HFSP before starting a faculty position in Quantitative Evolution at Imperial College London, Silwood Park campus. Matteo’s research integrates statistical modelling with evolutionary biology and population genetics to quantify the extent to which evolution has shaped complex phenotypes, including susceptibility to disease. He also develops and implements popular methods to process low-coverage DNA sequencing data (e.g., ngsTools). Matteo’s recent research aims at exploring the use of deep learning to detect adaptive evolution from genomic data with funding from The Leverhulme Trust.

Imperial College London - Dr. Matteo Fumagalli

https://www.imperial.ac.uk/people/m.fumagalli

Flora Jay profile photo

Flora Jay

Flora is a CNRS researcher (CR) at Université Paris-Saclay, LISN Laboratoire Interdisciplinaire des Sciences du Numérique (interdisciplinary computer science laboratory). She has a background in mathematical engineering and computer sciences. She develops statistical and machine learning-based methods to address biological questions, with major contributions to demographic inference using modern and ancient genomic data (J Stat Softw 2015; Nature 2014, PLoS Genet 2016). Her recent works cover Approximate Bayesian Computation and deep learning for population genetics (Mol Biol Evol 2019, Mol Ecol Res 2020), implementing a genetic simulator, generative networks, factor analysis for temporal DNA (Nat Comm 2020). She was awarded a French ANR Young Researchers, DIM-1-Health, and HFSP grants.

LRI - Mrs. Flora Jay

https://www.lri.fr/membre_en.php?mb=2132

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Aurélien Tellier

Aurélien is a theoretical evolutionary biologist and Professor for population genetics at TUM (Technical University of Munich) School of Life Sciences. His research focuses on developing population genomics theory and statistical inference methods using full genome data to study (i) coevolution between hosts (including humans) and their parasites (New Phytol 2019, PLoS Comp Biol 2020, bioRxiv 2021), and (ii) evolution under Markovian and non-Markovian models of dormancy (PNAS 2011, PLoS Genetics 2020, Mol Ecol Res 2021). He received fellowships from the A. von Humboldt and the Volkswagen foundations with current funding from the DFG and DAAD.

TUM School of Life Sciences - Prof. Dr. Aurélien Tellier

https://www.professoren.tum.de/en/tellier-aurelien