Dr. Kang's primary research interests are in developing statistical methods for large-scale complex biomedical data with application in precision medicine, imaging, epidemiology and genetics.
His research interests include 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.
Highlighted 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
