Research Interests


  • Statistical Methods for Large Scale Complex Data
  • Computational Algorithms for Big Data Analysis
  • Bayesian Methods
  • Gaussian Processes
  • Deep Neural Networks
  • Latent Source Seperation
  • Graphical Models
  • Spatial Point Process Modeling
  • High/Ultra-High Dimensional Variable Selection
  • Biomedical Imaging (fMRI, MRI, PET, DTI, CT and EEG)
  • Brain Computer Interfaces
  • Metabolomics
  • Bioinformatics
  • Statistical Genetics
  • Epidemiology
    • DTI Fiber Tracts

    Representative Publications


  • Wu B*, Guo Y, Kang J† (2024) Bayesian spatial blind source separation via the thresholded Gaussian process. Journal of the American Statistical Association (T&M), 119(545), 422-433
  • Lin Z*, Si Y, Kang J † (2024) Latent subgroup identification in image-on-scalar regression, Annals of Applied Statistics, 18(1), 468-486. (Presenting at Editor invited session in JSM 2024 )
  • Zhang D*, Li L, Sripada C, Kang J† (2023) Image response regression via deep neural networks, Journal of the Royal Statistical Society, Series B: Methodology, 85(5) 1589-1614
  • Zhao Y, Wu B*, Kang J (2023) Bayesian interaction selection model for multi-modal neuroimaging data analysis, Biometrics, 79(2):655-668.
  • Zhan T, Hartford A, Kang J, Offen W (2022) Optimizing graphical procedures for multiplicity control in a confirmatory clinical trial via deep learning. Statistics in Biopharmaceutical Research, 14(1):92-102. (Statistics in Biopharmaceutical Research Best Paper Award)
  • Ma T*, Li Y, Huggins J, Zhu J, Kang J† (2022) Bayesian inferences on neural activity in EEG-based brain-computer interface. Journal of the American Statistical Association (A&CS), 117:539, 1122-1133.
  • Guo C*, Kang J†, Johnson T† (2022) A spatial Bayesian latent factor model for image-on-image regression, Biometrics, 78(1):72-84. (Best Paper in Biometrics by an IBS Member Award)
  • He J*, Kang J† (2022) Prior knowledge guided ultra-high dimensional variable screening with application to neuroimaging data, Statistica Sinica, 32(4):2095-2117.
  • Morris E*, He K, Kang J† (2022) Scalar-on-network regression via boosting, Annals of Applied Statistics, 16(4):2755-2773.
  • Cai Q, Kang J, Yu T (2020) Bayesian variable selection over large scale networks via the thresholded graph Laplacian Gaussian prior with application to genomics. Bayesian Analysis, 15(1) 79-102. (Presenting at the Editor invited session in ISBA 2020)
  • Kang J, Reich BJ, Staicu AM (2018) Scalar-on-image regression via the soft thresholded Gaussian process, Biometrika, 105 (1) 165-184
  • Zhao Y*, Kang J, Long Q (2018) Bayesian multiresolution variable selection for ultra-high dimensional neuroimaging data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(2):537-550.
  • Kang J, Hong GH, Li Y (2017) Partition-based ultrahigh-dimensional variable screening, Biometrika, 104(4): 785-800. 
  • Kang J , Bowman FD, Mayberg H, Liu H (2016) A depression network of functionally connected regions discovered via multiattribute canonical correlation graphs. NeuroImage, 141:431-441.
  • Kang J, Nichols TE, Wager TD, Johnson TD (2014) A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis. Annals of Applied Statistics, 8(3): 1800-1824.
  • Kang J, Zhang N, Shi R (2014) A Bayesian nonparametric model for multivariate spatial binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study. Biometrics, 70(4):981-992.
  • Zhao Y, Kang J, Yu T (2014) A Bayesian nonparametric mixture model for selecting gene and gene-sub network. Annals of Applied Statistics, 8(2):999-1021.
  • Kang J, Johnson TD, Nichols TE, Wager TD (2011). Meta analysis of functional neuroimaging data via Bayesian spatial point processes. Journal of the American Statistical Association, 106(493):124--134.

    • Method Research Grants


  • SCH: New statistical learning methods for brain-computer interfaces
    Funded by: National Sciences Foundation (NSF-IIS): IIS2123777
    Role: Principal Investigator (Co-PI: Ji Zhu and Jane Huggins)
    Funding Period: 09/2021 - 08/2025
  • Bayesian Network Biomaker Selection in Metabolomics Data
    Funded by: National Institute of General Medical Sciences (NIGMS), R01-GM124061
    Role: Principal Investigator
    Funding Period: 03/2020 - 08/2022

  • Scalable Bayesian Methods for Big Imaging Data Analysis
    Funded by: National Institute of Drug Abuse (NIDA), R01-DA048993
    Role: Principal Investigator (MPI: Timothy D. Johnson)
    Funding Period: 09/2020 - 07/2025

  • Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data
    Funded by: National Institute of Mental Health (NIMH), R01-MH105561
    Role: Principal Investigator (MPI: Ying Guo)
    Funding Period: 09/2014 - 07/2025

    • Talk Slides


  • Bayesian Spatially Varying Weight Neural Networks with the Soft-Thresholded Gaussian Process Prior ( Slides )