SueYeon Chung

Computational neuroscience, neural network modeling and theory, population coding, neural population geometry, biologically plausible learning, neurophysics 

Our research strategy spans a variety of topics in theoretical neuroscience and theory of neural computation, ranging from neural population geometry to modern deep network theory. Our lab is currently focused on developing mathematical theories of neural population geometry and their applications to phenomena in neuroscience, and building neural networks and circuit models of brain functions. To do this, we use principles and methods in statistical physics, applied math, and machine learning. Our lab's work is jointly supported by the Center for Computational Neuroscience at the Flatiron Institute, an internal research division of the Simons Foundation.

Biography: Prior to joining NYU, SueYeon was a Postdoctoral Research Scientist in the Center for Theoretical Neuroscience at Columbia University, and a Fellow in Computation in the Department of Brain and Cognitive Sciences at MIT. Before that, She received a Ph.D. in applied physics at Harvard University. Before that, she studied physics and mathematics as an undergraduate at Cornell University.