I am an evolutionary biologist and population geneticist broadly interested in genome evolution and patterns of genetic diversity across genomes, populations, and species. My academic background combines a PhD in population genomics focused on recombination landscapes and ongoing research in comparative genomics and genome evolution, with a focus on understanding the diversity of structural variation across the eukaryotic tree of life. My work has allowed me to gain experience with Python/R programming in Unix environments, genomic data analysis, including variant calling and genotyping for a wide range of sequencing approaches (RAD-seq, RNA-seq, WGS, Pacbio long reads), and performing data analysis and population genetics inference, including simulations under diverse frameworks (msprime, SLiM). I have also actively engaged in meta-analyses to integrate large-scale genomic and life history trait datasets to understand evolutionary processes, such as the evolution of recombination landscapes across plant species.

During my PhD (ANR HotRec), I leveraged large genomic datasets in a comparative genomic framework to understand global patterns of molecular evolution in plants. I used meta-analytic approaches and advanced statistics to describe and quantify the diversity and determinants of recombination and its associated evolutionary processes (e.g., GC-biased gene conversion) among a large set of plant species.

After defending my PhD thesis in late 2022, I was rapidly involved in a wide range of themes spanning molecular evolution, population genomics, and evolutionary biology. My first postdoctoral position focused on testing an original theory of the evolution of gene expression (the runaway process proposed by Thomas Lenormand, ANR CisTransEvol). More recently, I have involved myself in the newly arising field of pangenomics and genomic structural variation (ERC funded, supervised by Claire Mérot). Among these widespread spectrum of topics, I have taken a broad view of population genomics, studying evolution at various levels of organization, including genes, genomes, populations, and species. I am interested in integrative approaches that reconcile different levels of organization under a more general and synthetic model (e.g., how patterns at the scale of genes could influence global genome evolution, and how both are co-evolving).

I am also a continuous learner, very curious and eager to train myself in a broad range of topics and methods in evolutionary genomics. I am involved in the development of bioinformatic pipelines and packages (structural variants calling, recombination maps) as well as the development of modern statistical methods for population genomics, especially Bayesian inference, Machine Learning and Neural Networks (ABC + Bayesian Neural Networks). In particular, I am actively developing Bayesian neural networks methods for inference of demographic parameters in an Approximate Bayesian Computation framework. Deep Learning is getting more and more attention in population genomics, and I believe it is a powerful versatile approach for inferences and predictions on large scale genomic data with complex underlying models and non-linear relationships in high-dimension datasets.

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