People
Shreya Saxena
Assistant Professor of Biomedical Engineering
Our ability to record large-scale neural and behavioral data has substantially improved in the last decade. However, the inference of quantitative dynamical models for cognition and motor control remains challenging due to their unconstrained nature. In the Saxena Lab, we incorporate constraints from anatomy and physiology to tame machine learning models of neural activity and behavior. We are interested in a constraints-based modeling approach that allows us to predictively understand the relationship between neural activity and behavior.
What do you do with Data Science?
We develop biologically inspired goal- and data- driven artificial intelligence methods to elucidate the neurodynamical basis of behavior, ranging from sensorimotor control to social behavior. Our aim is to better understand the brain using computational methods. We are broadly interested in topics on the interface of dynamical systems, control theory, and neuroscience.