Raymond Wong

Department of Statistics, Texas A&M University · associate professor
  • 401E Blocker Building, 155 Ireland Street
    College Station, TX 77843-3143
  • (979) 845-2992
  • raywong@tamu.edu

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.

Recent news

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

Research

Overview

My research is mostly problem-driven and has its roots from both scientific and engineering applications. These problems arise from astronomy, brain imaging, computer experiment and recommender system. And many of them involve modern data complications such as enormous volume, large dimensionality and manifold structures. Broadly speaking, I tackle them with low-rank and/or nonparametric modeling, combining with efficient computational techniques.

Research Interests

  • Low-rank modeling
  • Nonparametric and semi-parametric modeling
  • Statistical applications to astronomy, brain imaging, computer experiments and recommender systems
  • Statistical learning

Support

My research has been graciously supported by NASA and NSF.

Publications

Selected Publications See All Publications

★ represents student co-author; ♦ indicates alphabetical order
  • (2021+) "Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis". IEEE Transactions on Information Theory.
    Abstract Journal arXiv
  • (2021+) "Low-Rank Covariance Function Estimation for Multidimensional Functional Data". Journal of the American Statistical Association.
    ASA Section on Nonparametric Statistics Student Paper Award (J. Wang)
    Abstract Journal arXiv
  • (2019) "Provably Accurate Double-Sparse Coding". Journal of Machine Learning Research, 20(141), 1−43.
    Abstract Journal arXiv
  • (2019) "Partially Linear Functional Additive Models for Multivariate Functional Data". Journal of the American Statistical Association, 114(525), 406-418.
    Abstract Journal Supplement
  • (2019) "Matrix Completion with Covariate Information". Journal of the American Statistical Association, 114(525), 198-210.
    ICSA Student Paper Award (X. Mao)
    Abstract Journal Supplement
  • (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) "Kernel-based Covariate Functional Balancing for Observational Studies". Biometrika, 105(1), 199-213.
    Abstract Journal PDF Supplement Code
  • (2017) "Matrix Completion with Noisy Entries and Outliers". Journal of Machine Learning Research, 18(147), 1-25.
    Abstract Journal arXiv
  • (2017) "A Frequentist Approach to Computer Model Calibration". Journal of the Royal Statistical Society: Series B, 79(2), 635-648.
    Abstract Journal arXiv Supplement Code
  • (2016) "Fiber Direction Estimation, Smoothing and Tracking in Diffusion MRI". The Annals of Applied Statistics, 10(3), 1137-1156.
    Discussion Paper
    Abstract Journal arXiv PDF Supplement

Research Group

Current Members

  • Raymond Wong (Associate Professor of Statistics)
  • Sangyoon Yi (Postdoctoral Research Associate)
  • Jiayi Wang (Ph.D. Student)
  • Jianing Dong (Ph.D. Student)

Former Members

Ph.D. Students

  • Xiaojun Mao (Ph.D., 2018; with Song Xi Chen and Dan Nettleton): Topics in Matrix Completion and Genomic Prediction
       Now an Assistant Professor at Fudan University
  • Thanh Nguyen (Ph.D., 2020; with Chinmay Hegde): Provable Surrogate Gradient-based Optimization for Unsupervised Learning
       Now an Applied Scientist at Amazon

M.Sc. Students

  • Lukang Huang (M.Sc., 2019): Efficient Estimation of Counterfactual Distributions and Testing Distributional Treatment Effects
  • Ya Zhou (M.Sc., 2019): Broadcasted Nonparametric Tensor Regression

Teaching

Texas A&M University

    STAT 211: Principles of Statistics I
    2018-2019 Spring
    STAT 404: Statistical Computing
    2020 Spring
    STAT 612: Theory of Linear Models (graduate level)
    2017-2020 Fall
    STAT 616: Statistical Aspects of Machine Learning I: Classical Multivariate Methods (graduate level)
    2019 Fall

Iowa State University

    Stat 105: Introduction to Statistics for Engineers
    2016 Fall
    Stat 330: Probability & Statistics for Computer Science and Engineering
    2014-2015 Fall, 2017 Spring
    Stat 580: Statistical Computing (graduate level)
    2015-2017 Spring

University of California at Davis

    STA 13: Elementary Statistics
    2012 Summer
Powered by Hugo, modified from Resume theme. Raymond Wong 2014–2021.