Overview
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.
Current Duke Appointments & Affiliations
Postdoctoral Associate
Biostatistics & Bioinformatics, Division of Translational Biomedical,
Biostatistics & Bioinformatics
Recent Scholarly Works
Panel Flow Matching: A Generative Approach to Learning Distributions of Longitudinal Data
Preprint · June 27, 2026 Learning distributions of longitudinal data is central to tasks such as visualization, completion, classification, and synthetic data generation, but it remains statistically challenging because longitudinal observations are often irregular, sparse, and co ... Link to item CiteAssociating High-Dimensional Longitudinal Datasets through an Efficient Cross-Covariance Decomposition
Preprint · January 19, 2026 Link to item CiteFunctional-SVD for Heterogeneous Trajectories: Case Studies in Health
Journal article Journal of the American Statistical Association · January 1, 2026 Trajectory data, including time series and longitudinal measurements, are increasingly common in health-related domains such as biomedical research and epidemiology. Real-world trajectory data frequently exhibit heterogeneity across subjects such as patien ... Full text Open Access CiteRecent Grants
Integrated Detection and Classification of Sepsis via Tensor Methods Using EHR
ResearchPostdoctoral Associate · Awarded by National Heart, Lung, and Blood Institute · 2024 - 2029View All Grants