Scholar
Jianbin Tan
Google Scholar ID: ez9HH8kAAAAJ
Duke University
Citations & Impact
Citations
140
H-index
7
i10-index
6
Publications
18
Co-authors
0
Contact
Publications
Cited
0
Resume (English only)
Academic Achievements
- Smooth flow matching (Preprint)
- Functional singular value decomposition (Preprint)
- Sparse equation matching: a derivative-free learning for general-order dynamical systems (Preprint)
- A unified principal components analysis of stationary functional time series (Preprint)
- Integrated analysis for electronic health records with structured and sporadic missingness (Journal of Biomedical Informatics, 2025)
- Functional clustering for longitudinal associations between social determinants of health and stroke mortality in the U.S. (Annals of Applied Statistics, 2025)
- Green’s matching: an efficient approach to parameter estimation in complex dynamic systems (Journal of the Royal Statistical Society, Series B, 2024)
- Graphical principal component analysis of multivariate functional time series (Journal of the American Statistical Association, 2024)
- Age-related model for estimating the symptomatic and asymptomatic transmissibility of COVID-19 patients (Biometrics, 2023)
- Social mixing and network characteristics of COVID-19 patients before and after widespread interventions: A population-based study (Epidemiology & Infection, 2023)
- Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modelling study (Epidemiology & Infection, 2022)
- The effects of stringent and mild interventions for coronavirus pandemic (Journal of the American Statistical Association, 2021)
Research Experience
- Currently a Postdoctoral Associate in the Department of Biostatistics & Bioinformatics, Duke University, supervised by Prof. Anru Zhang and Prof. Pixu Shi.
Education
- PhD in Statistics from Sun Yat-sen University in 2023, advised by Prof. Hui Huang; Visiting student at the School of Management, USTC, working with Prof. Xueqin Wang in 2022.
Background
- Research interests: statistical learning for data with dynamic-, longitudinal-, or trajectory-based structures. Focuses on developing new methodologies for statistical learning of functions, flows, and differential equations, supporting effective analysis in biology, health, epidemiology, and environmental science.
Co-authors