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, generative modeling, and computational algorithms for data with dynamic or functional structures. Specifically, I focus on the statistical analysis of dynamic data, panel data, spatiotemporal data, high-dimensional longitudinal/functional data, and high-dimensional functional time series, with applications in biological, health, epidemiological, and environmental sciences.

Research Paper

Preprint

Functional singular value decomposition

Jianbin Tan, Pixu Shi, and Anru R. Zhang.

Preprint.

View on GitHub View on Arxiv
Preprint

Unified principal components analysis of stationary functional time series

Zerui Guo, Jianbin Tan (joint first authorship), and Hui Huang.

Preprint.

View on Arxiv
AOAS

Functional clustering for longitudinal associations between social determinants of health and stroke mortality in the U.S.

Fangzhi Luo, Jianbin Tan (joint first authorship), Donglan Zhang, Hui Huang, and Ye Shen.

Annals of Applied Statistics, 2025.

View on GitHub View on Arxiv
JRSSB

Green’s matching: an efficient approach to parameter estimation in complex dynamic systems

Jianbin Tan, Guoyu Zhang, Xueqin Wang, Hui Huang, and Fang Yao.

Journal of the Royal Statistical Society, Series B (Statistical Methodology), 2024.

View on GitHub View on Arxiv
JASA

Graphical principal component analysis of multivariate functional time series

Jianbin Tan, Decai Liang, Yongtao Guan, and Hui Huang.

Journal of the American Statistical Association, 2024.

View on GitHub View on Arxiv
I&E

Social mixing and network characteristics of COVID-19 patients before and after widespread interventions: A population-based study

Yuncong He, Leonardo Martinez, Yang Ge, Yan Feng, Yewen Chen, Jianbin Tan, Adrianna Westbrook, Changwei Li, Wei Cheng, Feng Ling, Huimin Cheng, Shushan Wu, Wenxuan Zhong, Andreas Handel, Hui Huang, Jimin Sun, and Ye Shen.

Epidemiology & Infection, 2023.

View on GitHub
BIOM

Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients

Jianbin Tan, Ye Shen, Yang Ge, Leonardo Martinez, and Hui Huang.

Biometrics, 2023.

View on GitHub
I&E

Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modelling study

Jianbin Tan, Yang Ge, Leonardo Martinez, Jimin Sun, Changwei Li, Adrianna Westbrook, Enfu Chen, Jinren Pan, Yang Li, Wei Cheng, Feng Ling, Zhiping Chen, Ye Shen, and Hui Huang.

Epidemiology & Infection, 2022.

View on medRxiv
JASA

The effects of stringent and mild interventions for coronavirus pandemic

Ting Tian, Jianbin Tan (joint first authorship), Wenxiang Luo, Yukang Jiang, Minqiong Chen, Songpan Yang, Canhong Wen, Wenliang Pan, and Xueqin Wang.

Journal of the American Statistical Association, 2021.

View on GitHub
JDS

Evaluate the risk of resumption of business for the states of New York, New Jersey and Connecticut via a pre-symptomatic and asymptomatic transmission model of COVID-19

Ting Tian, Jianbin Tan (joint first authorship), Yukang Jiang, Xueqin Wang, and Heping Zhang.

Journal of Data Science, 2021.

View on GitHub View on medRxiv