I am an incoming Assistant Professor in the Center for Neural Science at NYU (starting Fall 2022), jointly appointed as a Project Leader at the Flatiron Institute, Simons Foundation (started in Jan 2022). I am also an affiliated faculty member of the Center for Data Science and Cognition & Perception program at NYU.

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:

  1. analyzing geometries underlying neural or feature representations, embedding and transferring information

  2. developing neural network models and learning rules guided by neuroscience.

To do this, I use tools in statistical physics, machine learning, applied math, and statistics.

If you are a prospective graduate student, please apply directly to PhD programs at NYU (Neuroscience, Data Science, Psychology). Candidates for Postdoc Fellow, Research Assistant, Internship positions, or NYU graduate students interested in doing a rotation should email me with a CV and a brief description of research interests.

[CV] [Twitter]

Email: in the CV

News

Group

Postdocs
Abdulkadir Canatar
Jenelle Feather

Grad Students & Visiting Students
Sonica Saraf (Co-advised with Tony Movshon)
Sam Lippl (Larry Abbott Lab at Columbia U)
Joel Dapello (Jim DiCarlo Lab at MIT)

Research Assistants
Nga Yu Lo (Flatiron Research Analyst)
Albert Wakloo (Columbia Undergraduate Student)
Michael Kuoch
(MIT Undergraduate Student)

Selected Publications

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


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)


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]



All Publications

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


Transformation of acousting information to sensory decision variabiles in the parietal cortex

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

bioRxiv (2022)


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)


Conferences & Workshops

Invited Talks (Selected)

Teaching

Methods in Computational Neuroscience, 2019 (Marine Biology Laboratory, Woods Hole, MA)

Brains, Minds, Machines course, 2015 - 2017 (Marine Biology Laboratory, Woods Hole, MA)

MCB 131: Computational Neuroscience, 2013, 2015 (Harvard University) w/ Haim Sompolinsky

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

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

See also

Curriculum Vitae [PDF]

Machine Learning Tea at Harvard [Link]

Harvard Intelligent Probabilistic Systems Blog [Link]

Structure and Sequence Reading Group at Columbia [Link]