SueYeon Chung is an Assistant Professor in the Center for Neural Science at New York University and an Associate Research Scientist / Project Leader at the Flatiron Institute. Her research interests span a variety of topics in theoretical neuroscience and theory of neural computation, ranging from neural population geometry to modern deep network theory. Her lab is currently focused on developing mathematical theories of neural population geometry and their applications to phenomena in neuroscience, and building neural network and circuit models of brain functions. To do this, she uses principles and methods in statistical physics, applied math, and machine learning.
Prior to joining Flatiron, SueYeon was a Postdoctoral Research Scientist in the Center for Theoretical Neuroscience at Columbia University, where she was advised by Larry F. Abbott. Before that, she was a Fellow in Computation in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology, where she collaborated with Jim DiCarlo and Josh McDermott. Before that, she received a Ph.D. in applied physics at Harvard University, mainly supervised by Haim Sompolinsky and co-advised by Ryan P. Adams. Before that, she studied physics and mathematics as an undergraduate at Cornell University.