Graphical Principal Component Analysis of Multivariate Functional Time Series

My research interests lie in statistical learning for data with dynamic-, longitudinal-, or trajectory- based structures. Such data often exhibit complicated intrinsic mechanisms, dependencies, and heterogeneity, as well as challenges such as noise, irregular sampling, and high- or even infinite-dimensionality. To address these, I focus on developing new methodologies for statistical learning of functions, differential equations, and operators, supporting effective analysis in biology, health, epidemiology, and environmental science.