Simon has a background in quantitative genetics and plant breeding. During his PhD at UMR Quantitative Genetics and Evolution-Le Moulon (GQE) - Le Moulon, he studied how to account for genetic structure and admixture in association genetics and genomic prediction models in maize. During his postdoctorates, he worked on different aspects related to the optimization of experimental designs in a genomic prediction context: (i) multi-environment designs and (ii) highly structured training populations in oats. In parallel to these experiments in quantitative genetics, he was introduced to genomics by characterizing a diversity panel of grapevines for which the whole genome has been sequenced.

Within the SEG team, Simon will contribute to the understanding of the architecture and evolution of the sugarcane genome from sequencing data. He will participate in the development of bioinformatic tools adapted to the highly polyploid context. His skills in quantitative genetics will also be used to assist in the implementation of association genetics and genomic prediction in sugarcane breeding populations.