My research aims at articulating different data modalities ( cerebral, genetic, behavioral and environmental) using statistical tools derived from applied mathematics (network analysis, structural equation modeling, machine learning) to better characterize cognitive development and learning. 

I am particularly interested in the early factors that may play a role in neurocognitive development. 

My ultimate goal is to apply the knowledge and models developed during my thesis and postdoctoral work to advance research on prematurity. I aim to collaborate with neonatology and neonatal intensive care units to identify multilevel markers of prematurity and its consequences on cerebral and cognitive development. This will enable us to predict the developmental profile of each child and propose personalized care as early as possible. Through my work, I hope to contribute to reducing the academic difficulties faced by these children.