Théophile Sanchez studies how deep learning can be applied to DNA sequences to address fundamental questions in ecology and evolution. He completed his PhD in 2022, where he developed deep learning approaches for population genetics, with a particular focus on demographic inference and integrating sequence-level features into artificial neural network architectures. During his first postdoctoral work, he also explored this framework to environmental DNA, exploring how machine learning can enhance biodiversity monitoring and conservation efforts. Théophile’s current research focuses on leveraging genomic language models to better understand evolutionary processes and to identify unexpected or novel genomic variations.

Théophile joined the lab in February 2026 as a postdoctoral fellow.