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

  • 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), In Press.
  • Whiteman A, Bartsch A, Kang J, Johnson T (2022) Bayesian Inference for brain activity from functional magnetic resonance imaging collected at two spatial resolutions, Annals of Applied Statistics, In Press.
  • Morris E, He K, Kang J (2022) Scalar-on-network regression via boosting, Annals of Applied Statistics, In Press.
  • He J, Kang J. (2021) Prior guided ultra-high dimensional variable screening with application to neuroimaging data, Statistica Sinica, In Press.
  • Guo C, Kang J, Johnson T (2020) A spatial Bayesian latent factor model for image-on-image regression, Biometrics, In press.
  • 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
  • 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. 
  • Cai Q, Kang J, Yu T (2017) Network marker selection for untargeted LC/MS metabolomics data. Journal of Proteome Research, 16(3):1261-1269.
  • Kang J, Caffo B, Liu H (2016). Recent advances and challenges on big data analysis in neuroimaging. Frontiers in Neuroscience, Brain Imaging Methods, 10:505. doi: 10.3389/fnins.2016.00505
  • 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.
  • Bai Y, Kang J, Song P (2014) Efficient pairwise composite likelihood estimation for spatial-clustered data. Biometrics, 70(3):661-670.
  • 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.
  • Xue W, Kang J, Bowman FD, Guo J, Wager TD (2014) Identifying functional co-activation patterns in neuroimaging studies via Poisson graphical models. Biometrics, 70(4):812-822.
  • 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 )