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

Research Paper

Preprint

Smooth flow matching

Jianbin Tan, and Anru R. Zhang.

Preprint.

View on Arxiv
Preprint

Functional singular value decomposition

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

Preprint.

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Preprint

Sparse equation matching: a derivative-free learning for general-order dynamical systems

Jiaqiang Li, Jianbin Tan, and Xueqin Wang.

Preprint.

View on Arxiv
Preprint

Integrated analysis for electronic health records with structured and sporadic missingness

Jianbin Tan, Yan Zhang, Chuan Hong, T. Tony Cai, Tianxi Cai, and Anru R. Zhang.

Preprint.

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Preprint

A 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.

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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.

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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.

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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.

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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.

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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.

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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.

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