I am Raymond Wong, an assistant professor in the Department of Statistics, Texas A&M University. Before joining A&M, I was an assistant professor in the Department of Statistics, Iowa State University. I work on statistical problems with modern data complications such as big data, high dimensionality and manifold structures.

Current Teaching

  • Spring - Stat 211: Principles of Statistics I

Recent Papers

  • (2019) "On the Dynamics of Gradient Descent for Autoencoders". International Conference on Artificial Intelligence and Statistics (AISTATS).
    Earlier version 'Autoencoders Learn Generative Linear Models' appeared at ICML2018 workshop on Theoretical Foundations and Applications of Deep Generative Models
    Abstract arXiv
  • (2019) "Nonparametric Operator-Regularized Covariance Function Estimation for Functional Data". Computational Statistics & Data Analysis, 131, Special Issue on High-dimensional and Functional Data Analysis, 131-144.
    Abstract Journal arXiv Supplement Code
  • (2018+) "Partially Linear Functional Additive Models for Multivariate Functional Data". Journal of the American Statistical Association.
    Abstract Journal Supplement

All Papers