Hannah Choi is a postdoctoral researcher working with Eric Shea-Brown (University of Washington) and Stefan Mihalas (Allen Institute for Brain Science). Previously, she was awarded a Washington Research Foundation Innovation Postdoctoral Fellowship in Neuroengineering to study invariant visual coding in the ventral visual pathway based on predictive coding models, with Eric Shea-Brown and Anitha Pasupathy (University of Washington). In 2018, Hannah was awarded a Simons Berkeley Research Fellowship to spend a semester (The Brain and Computation program) at the Simons Institute for the Theory of Computing at the University of California, Berkeley. Before coming to the University of Washington, Hannah received her Ph.D. in Applied Mathematics from Northwestern University and her B.A. in Applied Mathematics from the University of California, Berkeley. For her PhD, Hannah studied the dynamics of retinal interneuron models, advised by Hermann Riecke and William Kath. Interested in linking network structure and neural computation, in her recent studies, Hannah investigated network synchrony in a spatially-embedded whole-brain connectome and developed an unsupervised method to construct mouse cortical and thalamic hierarchies. Currently, her research focuses on connecting biologically plausible local learning rules to global network properties, as well as testing predictive coding theory in mouse visual cortex.
Read the RFAs:
- BRAIN Initiative Advanced Postdoctoral Career Transition Award to Promote Diversity (K99/R00 Independent Clinical Trial Not Allowed)
- BRAIN Initiative Advanced Postdoctoral Career Transition Award to Promote Diversity (K99/R00 Independent Clinical Trial Required)
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