I am interested in understanding the computational principles that underlie sensorimotor learning in the human brain. My research aims to understand motor adaptation as a problem of inferring hidden states and parameters in an internal model of the world. By combining theory and experimentation, I hope to predict novel phenomena and explain existing observations.
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.
MATLAB code for the switching state-space model of motor adaptation can be found at GitHub.