Hannah Choi is currently a Washington Research Foundation Innovation Postdoctoral Fellow in Neuroengineering, working with Eric Shea-Brown, Wyeth Bair and Anitha Pasupathy, at the University of Washington. She is primarily interested in various topics in computational neuroscience, including optimal coding, dynamical systems, information theory, and mathematical modeling of single neurons and populations of neurons. Her recent projects aim to understand how visual information is processed in the ventral visual pathway of the brain. Specifically, Choi is interested in understanding the computational roles of feedback connections in the visual cortex, in processing complex visual stimuli. She also recently started a project investigating mouse brain connectivity data, with Stefan Mihalas at the Allen Institute for Brain Science.
Choi has a doctoral degree in Applied Mathematics from Northwestern University and a bachelor’s degree in Applied Mathematics from the University of California, Berkeley. For her PhD, Choi studied the dynamics of retinal interneurons by building multi-compartmental models of retinal interneurons based on electrophysiology; she was advised by Hermann Riecke and William Kath.