SueYeon Chung

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Postdoctoral Research Scientist, Center for Theoretical Neuroscience, Columbia University 
Research Affiliate, Fellow in Computation, Department of Brain and Cognitive Sciences, MIT 

My research interests are in the intersection of computational neuroscience and deep learning. Specifically, I am interested in understanding computation in the brain and artificial neural networks by analyzing geometries underlying neural or feature representations, embedding and transferring information. To do this, I use tools in theoretical physics, applied math, and computer science.

contact: sc4885 _at_ columbia _dot_ edu; 
sueyeon _at_ mit _dot_ edu



Education 
Ph.D. in Applied Physics, Harvard University 
        
Thesis Title: Statistical Mechanical Theory of Invariance Manifolds in Neural Networks
        Advisors: 
Haim Sompolinsky (Primary advisor), Ryan P. Adams (SEAS co-advisor) 
B.A. in Physics and Mathematics, Cornell University 

        Magna Cum Laude and Rawlings Presidential Research Scholar

Publications

Recent Talks
  • CNS 2019 Workshop: "Functional Network Dynamics: Recent Mathematical Perspectives", Barcelona, Spain, July 2019
  • EPFL Neuro Symposium: "Neuroscience Meets Deep Learning", Brain Mind Institute, Lausanne, Switzerland, July 2019 
  • C&T (Computation & Theory) Seminar Series, Janelia Research Campus, Ashburn, VA, June 2019  
  • Gatsby Tri-Center Meeting on Theoretical Neuroscience, UCL, London, UK, June 2019
  • NIMH/NIH Symposium: "From Neural Activity to Behavior: Computational Modeling of the Nervous System", Bethesda, MD, April 2019
  • Simons Collaboration on the Global Brain Boston-area Postdoc Meeting Series, Jan 2019 
  • Computational Tutorial, MIT Brain and Cognitive Sciences Department, Oct 2018  
  • CNS 2018 Workshop: "How does learning reshape dimensionality of collective network activity?", Seattle, WA, July 2018
  • COSYNE 2018 Workshop: "Manifold-splaining: what the theorist said to the experimentalist", Breckenridge, CO, Mar 2018
  • External Seminar, Gatsby Computational Neuroscience Unit at UCL, London, UK, Oct 2017    
  • CRC Robust Vision Symposium, Tübingen, Germany, Sep 2017 
  • Gatsby Tri-Center Meeting on Theoretical Neuroscience, Jerusalem, Israel, June 2017
  • COSYNE 2017 Workshop: "Deep Learning" and the brain: understanding neural representations with deep networks, Snowbird, UT, Feb 2017
  • Annual Sloan-Swartz Meeting for Computational Neuroscience, California Institute of Technology, Pasadena, CA, Aug 2016

Teaching

Brains, Minds, Machines course, Summer 2015 (Marine Biology Laboratory, Woods Hole, MA)
Brains, Minds, Machines course, Summer 2016 (Marine Biology Laboratory, Woods Hole, MA)
Brains, Minds, Machines course, Summer 2017 (Marine Biology Laboratory, Woods Hole, MA)
Methods in Computational Neuroscience, Summer 2019 (Marine Biology Laboratory, Woods Hole, MA) 


See also

Curriculum Vitae [PDF]
Machine Learning Tea at Harvard [Link]
Harvard Intelligent Probabilistic Systems Blog [Link