My research focuses on developing semiparametric and nonparametric functional methods and theory, leveraging methods such as smoothing splines, Gaussian processes, Dirichlet processes, and probabilistic machine learning. These methods are designed to address challenges arising in high-dimensional and correlated data analysis, including settings with contaminated or noisy data. I have also developed hybrid statistical frameworks that integrate both frequentist and Bayesian approaches to enhance model flexibility and inferential robustness.
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).