Taillefumier’s research in applied mathematics and theoretical neuroscience focuses on the emerging collective properties and possible design principles of neural networks. His approach combines ideas from optimization theory and stochastic dynamics on questions at the nexus of information theory and non-equilibrium thermodynamics, to better understand the nature of neural computations. Originally trained in Mathematical Physics at Ecole Polytechnique in France, Taillefumier completed his PhD in Biophysics at The Rockefeller University in 2012. Before joining UT Austin, Taillefumier was an Associate Research Scholar at the Lewis-Sigler Institute for Integrative Genomics at Princeton University.