Zhuo Wang (Jimmy)

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Research Interests

  • Machine Learning
  • Computational Neuroscience
  • Information Geometry

  • Publications

    Under Review
  • Invited to resubmit in Neural Computation
    Efficient Neural Codes that Minimize $L_p$ Reconstruction Error
    Z. Wang, A. Stocker and D. Lee.

  • Conference Proceedings
  • NIPS 2013
    Optimal Neural Population Codes for High-dimensional Stimulus Variables [ pdf, poster ]
    Z. Wang, A. Stocker and D. Lee. (2013). Lake Tahoe, NV, USA.

  • NIPS 2012
    Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss. [ pdf, poster ]
    Z. Wang, A. Stocker and D. Lee. (2012). Lake Tahoe, NV, USA.

  • Conference Abstracts
    * = available upon request
  • COSYNE 2015
    Learning Hierarchical Structure in Natural Images with Multiple Layers of Lp Reconstruction Neurons [ abstract*, poster* ]
    Z. Wang, A. Stocker and D. Lee. (2015). Salt Lake City, UT, USA.

  • COSYNE 2014
    Distinguishing Different Efficient Coding Principles from Tuning Curve Statistics [ abstract*, poster* ]
    Z. Wang, A. Stocker and D. Lee. (2014). Salt Lake City, UT, USA.

  • COSYNE 2014
    Efficient Coding Theory in Nonlinear Networks with Noise [ abstract*, poster* ]
    J. Tubiana, Z. Wang, D. Lee and H. Sompolinsky. (2014). Salt Lake City, UT, USA.

  • COSYNE 2013
    Optimal Neural Tuning for Arbitrary Stimulus Priors with Gaussian Input Noise. [ abstract*, poster* ]
    Z. Wang, A. Stocker, H. Sompolinsky and D. Lee. (2013). Salt Lake City, UT, USA.

  • COSYNE 2012
    Optimal Neural Tuning for Arbitrary Stimulus Priors. [ abstract*, poster* ]
    Z. Wang, K. Shi, A. Stocker and D. Lee. (2012). Salt Lake City, UT, USA.

  • Courses Taken

  • AMCS 601: Abstract Algebra
  • AMCS 602: Numerical Algebra
  • AMCS 608: Complex Analysis
  • AMCS 609: Functional Analysis
  • CIS 502: Analysis of Algorithms
  • CIS 520: Machine Learning
  • CIS 537: Medical Image (Audit)
  • CIS 581: Computer Vision
  • CIS 625: Computational Learning Theory
  • EAS 510: Academic Writting
  • ESE 605: Convex Optimization
  • ESE 650: Learning in Robotics
  • MATH 546: Probability Theory
  • MATH 547: Stochastic Process
  • MATH 600: Topology & Geometric Analysis I
  • MATH 644: Partial Differential Equations I (Audit)
  • MATH 660: Differential Geometry I
  • MATH 878: Probability & Algorithm Seminar
  • PHYS 585: Computational Neuroscience
  • STAT 541: Statistical Methods
  • STAT 910: Time Series Analysis
  • STAT 928: Statistical Learning Theory
  • STAT 955: Stochastic Calculus & Financial Apps.