I am an applied mathematician working at the intersection of systems neuroscience and machine learning. I develop and apply data-driven dynamical systems and dimensionality reduction tools to questions in neural and behavioral learning.

Currently a Professor in the Group for Neural Theory at the École Normale Supérieure in Paris, I received my PhD in applied mathematics at the University of Washington (under the supervision of Eric Shea Brown) where my dissertation focused on how the statistics of neural activity impact population coding. I then joined  Angus Silver's lab in University College London to study learning in the cerebellum and get my hands dirty with data analysis of large-scale neural data. 

My faculty research integrates these two sources of training with an aim to identify the fundamental principles of how task-relevant neural dynamics emerge over learning.