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SueYeon Chung
873 posts
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SueYeon Chung
@s_y_chung
assistant prof @harvardphysics @KempnerInst, trying to understand brains and neural networks w/ representation geometry & manifolds.
sychung.org
Joined November 2011
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  • Pinned
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    SueYeon Chung
    @s_y_chung
    Feb 10
    1/14 Our paper is out in @NatureNeuro! nature.com/articles/s4159… We develop a geometric theory of how neural populations support generalization across many tasks. Led by amazing @AlbertWakhloo @wslatton @ZuckermanBrain @FlatironInst @harvardphysics @KempnerInst
    30K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Sep 1, 2022
    First day as an assistant professor at NYU🔥Our lab is now up and running fully & jointly at NYU and Flatiron! Our lab's primary research interests lie at the intersection between theoretical neuroscience and machine learning. (1/)
  • user avatar
    SueYeon Chung
    @s_y_chung
    Jan 4, 2022
    👋So excited to share that I’ll be joining @NYU_CNS as Assistant Professor starting Sept'22 & joint appointment at @FlatironCCN starting this month! Our lab uses theory, computational models & neural data analysis to understand how the brain processes/represents information
  • user avatar
    SueYeon Chung
    @s_y_chung
    Dec 5, 2021
    Larry Abbott and I wrote a review on neural manifolds: "Neural population geometry: An approach for understanding biological and artificial neural networks"; available now at Current Opinion in Neurobiology, open access. doi.org/10.1016/j.conb… A thread👇 (1/)
  • user avatar
    SueYeon Chung
    @s_y_chung
    Jan 4, 2023
    🚨neuro-ML internship in NYC 2023🚨 Passionate about comp neuro, neuro-AI, neural network theory, neural manifolds 🧠🤖💻? Multiple summer intern openings at @FlatironCCN @SimonsFdn! To work with my group, mention my name in the app, and email me your CV & research interests.
    60K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Apr 16, 2021
    Speaking of opinionated reviews... a new review with LF Abbott on #NeuralManifolds: "Neural population geometry: An approach for understanding biological and artificial neural networks"
    arXiv logo
    arxiv.org
    Neural population geometry: An approach for understanding...
    Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the...
  • user avatar
    SueYeon Chung
    @s_y_chung
    Jul 10, 2018
    A new theory to analyze neural manifolds in high-dimensional data. We link the geometry of neural manifolds to their classification capacity. Exciting implications on understanding deep networks and sensory systems! With Haim Sompolinsky and Dan Lee. go.aps.org/2tVHcYm
  • user avatar
    SueYeon Chung
    @s_y_chung
    Feb 20, 2024
    Honored to have been selected as a #SloanFellow. Many thanks to the @SloanFoundation for recognizing our group's research, and to all my mentors, colleagues and members of my group. And @NYU_CNS @FlatironCCN @FlatironInst @SimonsFdn for the support. sloan.org/fellowships/20…
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    Sloan Foundation
    @SloanFoundation
    Feb 20, 2024
    We have today announced the names of the 2024 Sloan Research Fellows! Congratulations to these 126 outstanding early-career researchers: sloan.org/fellowships/20…
    53K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Sep 26, 2023
    Wouldn't it be great if we can reliably figure out why some ANNs predict neural data better than others? When models show similar neural predictivity, how can we differentiate between them? We show a practical & theoretical soln to this, using geometry and gen error theory.
    user avatar
    Jenelle Feather
    @jenellefeather
    Sep 26, 2023
    How do the spectral properties of a model influence neural prediction benchmarks? Check out our *new* paper, “A Spectral Theory of Neural Prediction and Alignment,” accepted to #NeurIPS2023 as a spotlight! arxiv.org/abs/2309.12821 w/ @canatar_a @s_y_chung @AlbertWakhloo 🧵1/11
    31K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Mar 25, 2020
    (1/n) The mean-field-theory based manifold analysis (MFTMA) is a technique that characterizes the geometry of neural object manifolds and connects it with the amount of the object information ('object manifold capacity') in distributed neural activity.
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    SueYeon Chung
    @s_y_chung
    Jul 13, 2023
    🎉Happy to share an exciting new theory paper our group, just published in @PhysRevLett with Editor's suggestion📖. Also featuring in Physics Magazine. Congratulations @AlbertWakhloo on this milestone! Title: Linear Classification of Neural Manifolds with Correlated Variability
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    Physics Magazine
    @PhysicsMagazine
    Jul 12, 2023
    A team led by @s_y_chung of @NYU_CNS & @FlatironCCN has developed a new method for estimating the performance capacity of a network. physics.aps.org/articles/v16/1…
    23K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Jun 14, 2023
    2023-2024 JOB ALERT📢: postdoc fellow position available in the Neuro-AI & Geometric Data Analysis group @FlatironInst @NYU_CNS! Join us in the thriving community of computational neuroscientists @FlatironCCN + NYU's amazing brain science & ML community.
    24K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Nov 7, 2023
    A great piece by @SimonsFdn about our theory on the capacity of neural manifolds with correlated variability, led by @AlbertWakhloo. Many thanks Mara Johnson-Groh & @LucyIkkanda.
    This Post is from an account that no longer exists. Learn more
    18K
  • user avatar
    SueYeon Chung
    @s_y_chung
    Dec 12, 2023
    Just arrived in New Orleans for #NeurIPS2023 this week. Topics I am excited about: neuro-AI, neural manifolds (representation geometry), stat physics for machine learning, interpretability, relational & causal representations My group is presenting their awesome work👇 (1/n)
    14K