My research interests focus on developing semiparametric and nonparametric functional methods and theory using smoothing, the Gaussian process, and the Dirichlet process to address issues in real data problems for high and low-dimensional correlated data analysis. I have developed hybrid statistical methods that combine frequentist and Bayesian methods.
My research areas are listed as
Semiparametric/nonparametric functional inference
Nonparametric Bayesian method/computing
Probabilistic machine learning/survival kernel machine learning
Functional graphical model and network estimation/Causal discovery
Functional variable selection for highly correlated variables
High-dimensional data analysis/Functional data analysis
Semiparametric mixed model/nonlinear mixed model/Longitudinal data analysis
Measurement error in high-dimensional covariates
My application areas are listed as
Functional magnetic resonance imaging (fMRI) for cognitive neuroscience
Biomedical engineering (Biosensing, Photonics, Optics)
Biostatistics (Environmental Health, Epidemiology, Public Health, Survival-life Time, Equine Medicine, and Various Cancer Metastasis)
Bioinformatics (Functional Genomics, System Biology, Protein-Protein Interaction, Proteomics).