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 big data size, high dimensionality and manifold structures. Broadly speaking, I tackle them with nonparametric and semi-parametric modeling, combining with efficient computational techniques.


My research is currently supported by National Science Foundation grants:

Research interests

  • Nonparametric and semi-parametric modeling
  • Regularization methods (e.g. $\ell_1$, $\ell_2$ and nuclear-norm penalty)
  • Statistical applications to astronomy, brain imaging, computer experiments and recommender systems
  • Statistical learning



  • (2018+) "Partially Linear Functional Additive Models for Multivariate Functional Data". Journal of the American Statistical Association, to appear.
    Abstract Journal
  • (2018+) "Matrix Completion with Covariate Information". Journal of the American Statistical Association, to appear.
    Abstract Journal
  • (2018) "Kernel-based Covariate Functional Balancing for Observational Studies". Biometrika, 105(1), 199-213.
    Abstract Journal PDF Supplement


  • (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 (with Discussions)". The Annals of Applied Statistics, 10(3), 1137-1156.
    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) "A Full Bayesian Approach for Boolean Genetic Network Inference". PLoS ONE, 9(12):e115806.
    Abstract Journal
  • (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.
    Abstract Journal Code


  • (2010) "Nonparametric Cepstrum Estimation via Optimal Risk Smoothing". IEEE Transactions on Signal Processing, 58(3), 1507-1514.
    Abstract Journal
  • (2010) "Structural Break Estimation of Noisy Sinusoidal Signals". Signal Processing, 90(1), 303–312.
    Abstract Journal



  • (2017) "Provably Accurate Double-Sparse Coding".
    Abstract arXiv
  • (2017) "Nonparametric Operator-Regularized Covariance Function Estimation for Functional Data".
    Abstract arXiv Code



  • (2018) "A Provable Approach for Double-Sparse Coding". AAAI Conference on Artificial Intelligence (AAAI). (Oral presentation)
    Abstract arXiv


  • (2017) "The Impact of Discharge Inversion Effect on Learning SRAM Power-Up Statistics". IEEE Asian Hardware Oriented Security and Trust Symposium (AsianHOST). (Oral presentation)
Made with Hugo, based on hugo-finite theme. Raymond Wong 2014–2018.