Jianbin Tan

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 Cite

Associating High-Dimensional Longitudinal Datasets through an Efficient Cross-Covariance Decomposition

Preprint · January 19, 2026 Understanding associations between paired high-dimensional longitudinal datasets is a fundamental yet challenging problem that arises across scientific domains, including longitudinal multi-omic studies. The difficulty stems from the complex, time-varying ... Link to item Cite

Smooth Flow Matching for Synthesizing Functional Data

Preprint · August 19, 2025 Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hi ... Link to item Cite

Sparse Equation Matching: A Derivative-Free Learning for General-Order Dynamical Systems

Preprint · July 26, 2025 Equation discovery is a fundamental learning task for uncovering the underlying dynamics of complex systems, with wide-ranging applications in areas such as brain connectivity analysis, climate modeling, gene regulation, and physical simulation. However, m ... Link to item Cite