# SueYeon Chung Assistant Professor of Physics and of Applied Mathematics ![headshot of SueYeon Chung](/sites/g/files/omnuum6476/files/styles/hwp_4_5__480x600/public/2025-01/Chung.png?itok=IgkM1ZUv) location\_on Northwest 195.05 smartphone [617-496-6506](tel:617-496-6506) email laptop\_windows [Group website](https://www.sychunglab.org/) SueYeon Chung is an Assistant Professor of Physics and of Applied Mathematics and an Institute Investigator at the Kempner Institute for the Study of Natural and Artificial Intelligence and the Center for Brain Science. She came to Harvard from the Center for Computational Neuroscience at the Flatiron Institute, where she led a research group while holding a joint appointment as an assistant professor at NYU. Previously, she was a postdoctoral fellow at the Center for Theoretical Neuroscience at Columbia University and a Fellow in Computation at MIT’s Department of Brain and Cognitive Sciences. Chung received her Ph.D. in applied physics at Harvard University and her B.A. in physics and mathematics at Cornell University. Chung’s research investigates the fundamental principles of neural computation in biological and artificial neural networks (ANNs), integrating concepts from statistical physics, machine learning, and neuroscience. Her work focuses on understanding how neural systems encode, transform, and process information through two complementary approaches. First, she develops theoretical frameworks to model the geometry and dynamics of neural population activity, focusing on the link between emergent structures in high-dimensional systems and the computations they implement. Second, she designs ANN-based models with neurally plausible architectures and biologically inspired learning rules to understand the principles underlying both biological and artificial intelligence. By connecting phenomena across scales—from individual neurons to population dynamics and emergent cognitive functions—Chung’s research seeks to uncover the principles underlying neural computation. This interdisciplinary approach bridges neuroscience and AI, revealing shared structures and functions in biological and artificial systems. These insights not only deepen our understanding of neural function but also inform the development of interpretable, efficient, and robust AI systems inspired by the brain’s computational strategies. At Harvard, Chung and her team will explore topics at the intersection of physics, neuroscience, and AI, including: ● Developing and applying theoretical and computational methods from statistical physics to analyze neural systems and artificial networks ● Uncovering the fundamental properties of neural representations that drive emergent functions and enable efficient computation across diverse tasks ● Investigating the geometric structures of neural manifolds to reveal shared principles between biological and artificial networks ● Studying how brain connectivity and learning shape information flow within and across brain regions ● Applying brain-inspired mechanisms to develop AI models that are robust, reliable and efficient in diverse environments > **Faculty Assistant:** [Molly Neylan](/people/molly-neylan "Molly Neylan") - ## Role [Faculty](/faculty)