I am interested in understanding the computational principles that underlie sensorimotor control and learning in the human brain. My research aims to understand motor adaptation as inference. I am currently developing a model of motor adaptation which consists in online state and parameter estimation in an internal generative model of perturbations in the environment. I hope that this model will be able to explain a wide range of seemingly disparate adaptation phenomena.

I also study the role of control points in the representations of object dynamics and the interaction between impedance control and motor adaptation.

More generally, I am interested in leveraging insights and methods from machine learning in neuroscience.