معرفی
SueYeon Chung is an Assistant Professor of Neural Science at New York University and a Project Leader at the Flatiron Institute's Center for Computational Neuroscience. She will join Harvard University in 2025 as an Assistant Professor in the Department of Physics, affiliated with the Kempner Institute for Natural and Artificial Intelligence and the Center for Brain Science. Her work bridges computational neuroscience and deep learning, focusing on neural computation principles in biological and artificial systems.
Education: Ph.D. in Applied Physics from Harvard University (2017), B.A. in Physics and Mathematics from Cornell University (2011). Postdoctoral fellowships at Columbia University and MIT.
Research Interests: Neural population geometry, representational learning, statistical physics of neural networks, and interdisciplinary approaches to understanding brain-inspired AI. She develops theoretical frameworks to analyze high-dimensional neural systems and designs biologically plausible neural network models.
Recent Work Trends: Focus on representation geometry, manifold capacity, and task-driven neural dynamics. Recent articles explore feature learning beyond traditional dichotomies, alignment with primate visual cortex, and spectral theories of neural networks. Her work emphasizes geometric structures in both biological and artificial systems.
Awards: NeurIPS Spotlight Presentations (2021, 2023), Physical Review Letters Editors' Suggestion (2023), ICML Best Paper Award (2020).
Grants & Advising: Active in interdisciplinary grants spanning neuroscience and physics. Advises on computational neuroscience projects and co-organizes workshops (e.g., Analytical Connectionism Summer School, CCN Junior Workshop). Teaches courses in statistical mechanics and computational neuroscience.
Labs & Affiliations: Leads research groups at NYU and Flatiron Institute. Collaborates with the CILVR Group and Harvard’s Kempner Institute. Active in conferences like COSYNE and NeurIPS, delivering keynote talks globally.
