Raymond Wong

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

Raymond is an assistant professor in the Department of Statistics, Texas A&M University. Before joining Texas A&M, he was an assistant professor in the Department of Statistics, Iowa State University. He is currently an associate editor of the Canadian Journal of Statistics. His research focuses on statistical problems with modern data complications such as enormous volume, large dimensionality and manifold structures.

Recent news

June 2020: Paper on 'Median Matrix Completion: from Embarrassment to Optimality' has been accepted to International Conference on Machine Learning (ICML).   link
May 2020: Raymond will be promoted to Associate Professor with tenure, effective September 1, 2020.  
April 2020: Thanh has passed his Ph.D. final oral exam. He will be an Applied Scientist at Amazon starting this June. Congratulations!  
March 2020: Paper on 'Matrix Completion under Low-Rank Missing Mechanism' has been accepted to Statistica Sinica.   link
March 2020: Raymond has started his two-year term as the Awards Chair of the ASA Sections on Statistical Computing and Statistical Graphics.  
January 2020: Paper on 'Network Estimation via Graphon with Node Features' has been accepted to IEEE Transactions on Network Sciences and Engineering.   link
January 2020: Jiayi has been selected as a winner of the Student Paper Award by the ASA Section on Nonparametric Statistics. Congratulations!  
December 2019: Thanh has won the Research Excellence Award from the Graduate College of Iowa State University. Congratulations!  
November 2019: Paper on 'Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis' is now on arXiv.   link
October 2019: Raymond is now an executive member of the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS).   link
September 2019: Paper on 'Provably Accurate Double-Sparse Coding' has been accepted to Journal of Machine Learning Research.   link



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


My research has been graciously supported by the following external grants:


  • NSF DMS-1711952 (via a subcontract; Co-I): Covariate Balancing in Missing Data and Observational Studies (2018-2020)
  • NASA 80NSSC19K0656 (Co-I): Virtual Assistant for Spacecraft Anomaly Treatment During Long-Duration Exploration Missions (2019-2023)


  • NSF DMS-1612985 / DMS-1806063 (PI): Collaborative Research: New Directions in Multidimensional and Multivariate Functional Data Analysis (2017-2020)


Selected Publications See All Publications

★ represents student co-author; ♦ indicates alphabetical order
  • (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
  • (2016) "Detecting Abrupt Changes in the Spectra of High-energy Astrophysical Sources". The Annals of Applied Statistics, 10(2), 1107-1134.
    Abstract Journal arXiv PDF Code
  • (2014) "Automatic Estimation of Flux Distributions of Astrophysical Source Populations". The Annals of Applied Statistics, 8(3), 1690-1712.
    Abstract Journal arXiv PDF Supplement
  • (2014) "Robust Estimation for Generalized Additive Models". Journal of Computational and Graphical Statistics, 23(1), 270–289.
    ASA Section on Nonparametric Statistics Student Paper Award
    Abstract Journal Code

Research Group

Current Members

  • Raymond Wong
  • Jiayi Wang (Ph.D. student)
  • Jianing Dong (Ph.D. student)

Former Members

Ph.D. Students

  • Xiaojun Mao (Ph.D., 2018; co-supervision 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; co-supervision 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)
  • Ya Zhou (M.Sc., 2019)


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 2014–2020.