Raymond Wong

  • Associate Professor, Department of Statistics, Texas A&M University
Raymond is an Associate Professor in the Department of Statistics, Texas A&M University. His research focuses on statistical problems with modern data complications such as enormous volume, large dimensionality and manifold structures.
He is an Executive Committee Member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS) funded by the NSF TRIPODS Initiative. He is also an Associate Editor of the Canadian Journal of Statistics and the Journal of Computational and Graphical Statistics. In addition, he serves as the Awards Chair of both the ASA Section on Statistical Computing and the ASA Section on Statistical Graphics.

News

  • May 2021: Our paper on 'Matrix Completion with Model-free Weighting' has been accepted by the International Conference on Machine Learning (ICML).   [link]
  • March 2021: Our paper on 'Estimation of Partially Conditional Average Treatment Effect by Hybrid Kernel-covariate Balancing' is now on arXiv.   [link]
  • February 2020: Our paper on 'Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis' has been accepted by IEEE Transactions on Information Theory.   [link]
  • January 2021: Raymond has been appointed as an Associate Editor of the Journal of Computational and Graphical Statistics.  
  • December 2020: Our paper on 'Tensor Linear Regression: Degeneracy and Solution' has been accepted by IEEE Access. The previous title of this paper is 'CP Degeneracy in Tensor Regression'.   [link]
  • October 2020: Raymond has been appointed as the Awards Chair (an officer) of the ASA Sections on Statistical Computing and Statistical Graphics. His term lasts until February 2023.  
  • September 2020: Our paper on 'A Wavelet-Based Independence Test for Functional Data with an Application to MEG Functional Connectivity' is now on arXiv.   [link]
  • September 2020: Raymond has been identified as a Top 33% Reviewer for International Conference on Machine Learning (ICML) 2020.  
  • September 2020: Our paper on 'Low-Rank Covariance Function Estimation for Multidimensional Functional Data' has been accepted by the Journal of the American Statistical Association.   [link]
  • September 2020: Our paper on 'Broadcasted Nonparametric Tensor Regression' is now on arXiv.   [link]