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.

[CV] [Twitter][Lab]

Email:
schung at flatironinstitute dot org; 

sueyeon at nyu dot edu

News

Selected Publications

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

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)

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]

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]


All Publications

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


Statistical Mechanics of Support Vector Regression
Abdulkadir Canatar, SueYeon Chung

arXiv:2412.05439 (2024)

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

Thomas Yerxa, Jenelle Feather, Eero Simoncelli, SueYeon Chung

NeurIPS (2024)

Representational Learning by Optimization of Neural Manifolds in an Olfactory Memory Network
Bo Hu, Nesibe Z. Temiz, Chi-Ning Chou, Peter Rupprecht, Claire Meissner-Bernard, Benjamin Titze, SueYeon Chung, Rainer W. Friedrich
bioRxiv 2024.11.17.623906 (2024)

Nonlinear classification of neural manifolds with contextual information

Francesca Mignacco, Chi-Ning Chou, SueYeon Chung

arXiv:2405.06851 (2024)

Neural Manifold Capacity Captures Representation Geometry, Correlations, and Task-Efficiency Across Species and Behaviors

Chi-Ning Chou, Luke Arend, Albert J. Wakhloo, Royoung Kim, Will Slatton, SueYeon Chung
bioRxiv 2024.02.26.582157 (2024)

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

Albert J. Wakhloo, Will Slatton, SueYeon Chung

arXiv:2402.16770 (2024) 


Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds

Michael Kuoch*, Chi-Ning Chou*, Nikhil Parthasarathy, Joel Dapello, James J. DiCarlo, Haim Sompolinsky, SueYeon Chung

Conference on Parsimony and Learning (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]

Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry

Andrew Ligeralde*, Yilun Kuang*, Thomas Yerxa, Miah N Pitcher, Marla Feller, SueYeon Chung

NeurIPS Workshop on Unifying Representations in Neural Models (UniReps) (2023) [arXiv version]

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Eliott R.J. Levy, Simon Carrillo-Segura, Eun Hye Park, William T. Redman, José R. Hurtado, SueYeon Chung, André A. Fenton

Cell Reports (2023)

Social learning enhances stimulus representations in the auditory cortex

Nihaad Paraouty, Justin D. Yao, Léo Varnet, Chi-Ning Chou, SueYeon Chung, Dan H. Sanes 

Nature Communications (2023)

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

Unveiling the benefits of multitasking in disentangled representation formation

Jenelle Feather, SueYeon Chung 

Trends in Cognitive Sciences (2023)

Transformation of acoustic information to sensory decision variables in the parietal cortex 

Justin D. Yao*, Klavdia O Zemlianova*, David L Hocker, Cristina Savin, Christine M Constantinople, SueYeon Chung, Dan H Sanes 

PNAS (2023) 


The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks

Sam Lippl, L.F. Abbott, SueYeon Chung

arXiv:2022.02649 (2022) 


Divisive Feature Normalization Improves Image Recognition Performance in AlexNet

Michelle Miller, SueYeon Chung, Kenneth D. Miller

ICLR (2022)


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

SueYeon Chung, L.F. Abbott 

Current Opinion in Neurobiology (2021)


Credit Assignment Through Broadcasting a Global Error Vector

David G. Clark, L. F. Abbott, SueYeon Chung

NeurIPS (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) 


Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models 

Matteo Alleman, Jonathan Mamou, Miguel A Del Rio, Hanlin Tang, Yoon Kim+, SueYeon Chung+ 

ACL Workshop, Representation Learning for NLP (2021)  


Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks

Landan Seguin, Anthony Ndirango, Neeli Mishra, SueYeon Chung, Tyler Lee 

arXiv:2108.12001 (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] 


On 1/n Neural Representation and Robustness

Josue Nassar*, Piotr Sokol*, SueYeon Chung, Kenneth Harris, Memming Park  

NeurIPS (2020)


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]


Learning Data Manifolds with a Cutting Plane Method 

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

Neural Computation (2018)


Statistical Mechanics of Neural Processing of Object Manifolds

SueYeon Chung

Doctoral dissertation, Harvard University (2017) 


Linear Readout of Object Manifolds

SueYeon Chung, Daniel D. Lee, Haim Sompolinsky

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


Small Molecule Injection into Single-Cell C. elegans Embryos via Carbon-Reinforced Nanopipettes 

Lucy D. Brennan, Thibault Roland, Diane G. Morton, Shanna M. Fellman, SueYeon Chung, Mohammad Soltani, Joshua W. Kevek, Paul M. McEuen, Kenneth J. Kemphues, and Michelle D. Wang

PLoS ONE (2013)  


Invited Talks (Selected)

Teaching

Graduate Seminar, "Neural Networks: Theory & Applications", Spring 2024 (NYU)
Methods in Computational Neuroscience, 2019 (Teaching Assistant), 2024 (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)