Im a Junior Researcher in the Institute of Evolutionary Sciences, in the university of Montpellier 2 (France), currently detached at the Max Planck Institute for Evolutionary Biology in Plön (Germany), in the department of Evolutionary Genetics headed by Prof. Dr. Diethard Tautz. In 2011-2014, I was detached at the Max Planck Institute for terrestrial Microbiology in Marburg, in the department of Organismic Interactions, headed by Prof. Dr. Regin Kahmann.
I did my ph-D under the supervision of Nicolas Galtier and Pierre Boursot, in the "Génome, populations, interactions, adaptation" laboratory, in the university of Montpellier 2. I defended in 2006, November 14th, under the title "Phylogenetic and Bioinformatic approaches to detect coevolution at the molecular level". I developed a new probabilistic method to map substitutions onto a kno wn phylogeny and used it to detect, in an alignment of homologous sequences, positions which underwent a non-independent evolutionary history. This method has proved to be powerful and recover, without any prior k nowledge of the structure, a significant amount of ribosomal RNA secondary structure. By extending it to the protein case, I was able to show that it succeeded in detecting convincing groups of coevolving position s. Several detected positions therefore appeared to have been reported as being under positive selection, or to be in contact in the 3-dimensional structure.
After my PhD, I used my extra year of funding as a post-doc in the Institut des Sciences de l'Evolution de Montpellier, in the team of molecular phylogeny, directed by Pr. Emmanuel Douzery. Using the s ubstitution mapping procedure, I developed a new method to detect positions in an alignment that are under biochemical constraint. During this period, I spent 2 month in Tal Pupko's lab, in Tel Aviv.
From September 2007 to Janaury 2010, I have been a post-doc at the Bioinformatics Research Center, in the university of Aarhus (Denmark), under the supervision of Mikkel Schierup. There I developed new mod els for genome analysis in a population genetics perspective. These models rely on the coal-HMM framework, developped in the lab by Hobolth A et al (PLoS Biol. ). It uses a Hidden Markov Model along a genomic alig nment, the hidden states of the chain being different genealogies. Such model can use the information contained in the high number of sites to infer population genetics parameters like speciation time or ancestral population sizes. I'm Developing new models and statistic procedures to infer past demographic and selective events.