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
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 )