Jonathan Vacher was a student at École Normale Supérieure de Cachan (now Saclay) where he pursued a degree in applied mathematics. He completed his master's degree with a specialty in computational imaging and machine learning (Master MVA). He started a multidisciplinary PhD in mathematics (Paris Dauphine University, CEREMADE) and neuroscience (CNRS, UNIC) under the supervision of Gabriel Peyré and Cyril Monier. Then, Jonathan was a postdoc at Albert Einstein College of Medicine in New-York between 2017 and 2020 while initiating a collaboration with Pascal Mamassian from LSP.
Jonathan is interested in developing mathematically grounded visual stimulation models to probe visual perception in both psychophysics and electrophysiology. He is close to experimenters and knows how to run experiments in psychophysics. He is familiar with many kinds of data (natural images and sounds, extra-cellular recording, VSDi, psychometric responses) and he is able to handle most machine learning approaches (probablistic models, supervised learning including deep learning, unsupervised learning) to analyze them. Jonathan is mostly interested in showing how the brain is approximating probabilities and how these probabilities are represented by neural populations.