Jian Kang, PhD, MS

• PhD, University of Michigan, 2011
• MS, Tsinghua University, 2007
• BS, Beijing Normal University, 2005

Research Interests:
Imaging data analysis, Bayesian methods, efficient statistical computation algorithms, ultrahigh-dimensional feature selection, latent source separation methods, graphical models, network inference, composite likelihood approach and missing data problems.

Research Projects:

• New statistical learning methods for brain-computer interfaces
• Scalable Bayesian methods for big imaging data analysis
• Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data
• Bayesian network biomarker selection in metabolomics data

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 the 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.

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.

M4055 SPH II
1415 Washington Heights
Ann Arbor, MI 48109

Email: jiankang@umich.edu
Office: 734-763-1607

For media inquiries: sph.media@umich.edu