My research interests focus on developing semiparametric and nonparametric functional methods and theory, utilizing smoothing, the Gaussian process, the Dirichlet process, and probabilistic machine learning to address issues in real data problems involving high-dimensional correlated data/ contaminated 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, network/causal discovery, and Markov Random Field
Functional variable selection for highly correlated variables
High-dimensional functional data analysis
Semiparametric mixed model/nonlinear mixed model/functional 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): Brain/Breast tumor cell line, real-time and streaming signal data
Biostatistics (Environmental Health, Epidemiology, Public Health, Survival-life Time, Equine Medicine, and Various Cancer Metastasis)
Bioinformatics (Functional Genomics, System Biology, Protein-Protein Interaction, Proteomics).