I am a theoretical neuroscientist studying the structure of animal behavior and the neural mechanisms of flexible and adaptive behavior control, using tools from dynamical systems, statistical modeling, and machine learning.

The core goals of my research program are:
To develop new theories for the distributed control of behavior by multiple recurrently connected neural populations.
To understand computation in heterogeneous neural populations with diverse cell types and signaling molecules, by building and training biologically constrained neural population models.
To construct richer descriptions of animal behavior and movement using pose estimation and novel supervised/unsupervised machine learning tools.