Jianbin Tan | Duke University

Welcome to Jianbin's homepage!

I am currently a Postdoctoral Associate in the Department of Biostatistics & Bioinformatics, Duke University, supervised by Prof. Anru Zhang and Prof. Pixu Shi. I received my PhD in Statistics from Sun Yat-sen University in 2023, advised by Prof. Hui Huang. I was a visiting student working with Prof. Xueqin Wang in the School of Management, USTC, in 2022.

My research interests lie in statistical learning for data with dynamic, longitudinal, or functional structures. Such data often exhibit complicated dependencies and heterogeneity, as well as challenges arising from irregular sampling and high- or infinite-dimensionality. To address these, I focus on developing new methodologies for learning with functions, opearators, and differential equations, supporting effective analysis in health, epidemiology, and environmental science.

Application

Projects organized by application domains.

Health Data Analysis

This project develops statistical methods for electronic health records with complex missingness and irregular sampling, with an emphasis on generative modeling, representation learning, structure-aware imputation, and reliable downstream tasks for prediction and inference.

Representative publications:

Smooth Flow Matching Preprint

Functional-SVD for Heterogeneous Trajectories: Case Studies in Health JASA

Integrated Analysis for Electronic Health Records with Structured and Sporadic Missingness JBI

Epidemiologic Data Analysis

This project develops statistical methods for dynamic epidemiologic and public health data, including causal inference, transmission modeling, and longitudinal association discovery, to characterize time-varying effects and enable reliable inference and public health evaluation.

Representative publications:

The Effects of Stringent and Mild Interventions for Coronavirus Pandemic JASA

Transmission Roles of Symptomatic and Asymptomatic COVID-19 Cases: A Modelling Study I&E

Functional Clustering for Longitudinal Associations between Social Determinants of Health and Stroke Mortality in the U.S. AOAS

Age-Related Model for Estimating the Symptomatic and Asymptomatic Transmissibility of COVID-19 Patients BIOM

Environmental Data Analysis

This project develops statistical methods for environmental daily curves (commonly modeled to as functional time series), which exhibit multi-way dependencies due to complicated environmental measurement processes. We focus on frequency-domain approaches for dependence-aware inference, enabling tasks such as prediction, graph/network inference, and data integration in environmental applications.

Representative publications:

Graphical Principal Component Analysis of Multivariate Functional Time Series JASA

A Unified Principal Components Analysis of Functional Time Series Preprint

A Frequency-Domain Approach for Integrating Multiple Functional Time Series STAT

Statistical Method

Projects organized by methodological themes.

Functional/Longitudinal Data Analysis

This research develops statistical learning methods for functional/longitudinal data under complicated settings. We emphasize model-free approaches that provide structural adaptivity and accommodate irregular sampling—key challenges in modern functional/longitudinal data analysis.

Representative publications:

Smooth Flow Matching Preprint

Functional-SVD for Heterogeneous Trajectories: Case Studies in Health JASA

Graphical Principal Component Analysis of Multivariate Functional Time Series JASA

Functional Clustering for Longitudinal Associations between Social Determinants of Health and Stroke Mortality in the U.S. AOAS

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

A Unified Principal Components Analysis of Functional Time Series Preprint

Statistical Modeling/Learning of Differential Equations

This research focuses on developing statistical methods for differential equation modeling and learning, including parameter estimation and vector field inference, as well as differential-equation–based generative modeling and other related tasks.

Representative publications:

Green’s Matching: an Efficient Approach to Parameter Estimation in Complex Dynamic Systems JRSSB

Smooth Flow Matching Preprint

The Effects of Stringent and Mild Interventions for Coronavirus Pandemic JASA

Age-Related Model for Estimating the Symptomatic and Asymptomatic Transmissibility of COVID-19 Patients BIOM

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