I am an Assistant Professor of Neural Science at New York University, and am jointly appointed as a Project Leader at the Center for Computational Neuroscience, Flatiron Institute, Simons Foundation. I am also part of the CILVR (Computational Intelligence, Learning, Vision, and Robotics) Group, and an affiliated faculty member at the Center for Data Science, and Cognition & Perception program at New York University.   

My research interests are at the intersection of computational neuroscience and deep learning. I am interested in understanding computation in the brain and artificial neural networks by:

To do this, I use tools in statistical physics, machine learning, and high-dimensional geometry & statistics.

[Update] In July 2025, I will be joining Harvard University as an Assistant Professor in the Department of Physics. I will also serve as an Institute Investigator at the Kempner Institute for Natural and Artificial Intelligence, a faculty member in the Center for Brain Science, and hold a secondary appointment in Applied Mathematics within the School of Engineering and Applied Sciences.

[CV] [Twitter][Lab]

Email:
schung at flatironinstitute dot org; 

sueyeon at nyu dot edu

News

Selected Publications  [all publications]

(*: co-first, +: co-last)


Statistical Mechanics of Support Vector Regression
Abdulkadir Canatar, SueYeon Chung

arXiv:2412.05439

Neural Population Geometry and Optimal Coding of Tasks with Shared Latent Structure

Albert J. Wakhloo, Will Slatton, SueYeon Chung

arXiv:2402.16770

Geometry Linked to Untangling Efficiency Reveals Structure and Computation in Neural Populations

Chi-Ning Chou, Royoung Kim, Luke A. Arend, Yao-Yuan Yang, Brett D. Mensh, Won Mok Shim, Matthew G. Perich, SueYeon Chung
bioRxiv 2024.02.26.582157

The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models

Artem Kirsanov, Chi-Ning Chou, Kyunghyun Cho, SueYeon Chung

NAACL (2025) [in press]

Nonlinear classification of neural manifolds with contextual information

Francesca Mignacco, Chi-Ning Chou, SueYeon Chung

Physical Review E (2025)
Selected for Editors' Suggestion

Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT

Thomas Yerxa, Jenelle Feather, Eero Simoncelli, SueYeon Chung

NeurIPS (2024)

A Spectral Theory of Neural Prediction and Alignment 

Abdulkadir Canatar*, Jenelle Feather*, Albert Wakhloo, SueYeon Chung

NeurIPS (2023) [arXiv version]
Selected for Spotlight Presentation  

Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations

Thomas Yerxa, Yilun Kuang, Eero Simoncelli, SueYeon Chung

NeurIPS (2023) [arXiv version]

Linear Classification of Neural Manifolds with Correlated Variability

Albert Wakhloo, Tamara J. Sussman, SueYeon Chung 

Physical Review Letters (2023) [arXiv version] [Viewpoint]
Selected for Editors' Suggestion, Featured in Physics


Neural population geometry: An approach for understanding biological and artificial neural networks

SueYeon Chung, L.F. Abbott 

Current Opinion in Neurobiology (2021)


Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception 

Joel Dapello*, Jenelle Feather*, Hang Le*, Tiago Marques, David Cox, Josh McDermott, Jim DiCarlo, SueYeon Chung 

NeurIPS (2021) 


On the Geometry of Generalization and Memorization in Deep Neural Networks

Cory Stephenson*, Suchismita Padhy*, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung

ICLR (2021) 


Emergence of Separable Manifolds in Deep Language Representations

Jonathan Mamou*, Hang Le*, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung

ICML (2020) [Supplementary Materials] [Code]


Separability and Geometry of Object Manifolds in Deep Neural Networks

Uri Cohen*, SueYeon Chung*, Daniel D. Lee, Haim Sompolinsky  

Nature Communications (2020) [Supplementary Materials] [BioRxiv version] [Code] 


Untangling in Invariant Speech Recognition

Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz Elibol, Hanlin Tang, Josh McDermott, SueYeon Chung

NeurIPS (2019) [Supplementary Materials] [Code]


Classification and Geometry of General Perceptual Manifolds

SueYeon Chung, Daniel D. Lee, Haim Sompolinsky

Physical Review X (2018) [Supplementary Materials]


Linear Readout of Object Manifolds

SueYeon Chung, Daniel D. Lee, Haim Sompolinsky

Physical Review E, Rapid Communications (2016) [Supplementary Materials]


Recent Invited Talks [all invited talks]

Teaching

Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) Summer School, 2025 (Cambridge, MA)
Graduate Seminar, "Neural Networks: Theory & Applications", Spring 2024 (NYU)
Methods in Computational Neuroscience, 2019 (Teaching Assistant), 2024-2025 (Course Lecturer)  (Woods Hole, MA)
Brains, Minds, Machines course, 2015-2017 (Teaching Assistant), 2024 (Guest Lecturer) (Woods Hole, MA)

MCB 131: Computational Neuroscience, 2013, 2015  (Harvard University) w/ Haim Sompolinsky [also known as NEURO 131/PHYSICS 131]

APMTH 147: Nonlinear Dynamics, Fall 2011 (Harvard University)

PHYS 213: Electricity and Magnetism, Summer 2009 (Cornell University)