Raymond Wong

Department of Statistics, Texa A&M University ยท 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.

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 is currently supported by the following grants:

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

Publications

Selected Publications

  • (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
  • (2018) "Kernel-based Covariate Functional Balancing for Observational Studies". Biometrika, 105(1), 199-213.
    Abstract Journal PDF Supplement Code
  • (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

Students

Completed

  • 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

In progress

  • Thanh Nguyen (Ph.D.; co-supervision with Chinmay Hegde)
  • Jiayi Wang (Ph.D.)
  • Lukang Huang (M.Sc.)
  • Ya Zhou (M.Sc.)

Teaching

Texas A&M University

    STAT 211: Principles of Statistics I
    2018 Spring, 2019 Spring
    STAT 612: Theory of Linear Models (graduate level)
    2017 Fall, 2018 Fall, 2019 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 Fall, 2015 Fall, 2017 Spring
    Stat 580: Statistical Computing (graduate level)
    2015 Spring, 2016 Spring, 2017 Spring

University of California at Davis

    STA 13: Elementary Statistics
    2012 Summer
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