Basic Info:
Dr. Zhan is a Professor and Doctoral Supervisor at the School of Statistics and Data Science, Southeast University. He obtained his Ph.D. in Statistics from Pennsylvania State University, USA, in 2015. From 2017 to 2021, he served as an Assistant Professor at the Department of Public Health, Pennsylvania State University. Subsequently, from 2021 to 2024, he held the position of Associate Professor at the School of Public Health and Beijing International Center for Mathematical Research at Peking University. Since 2024, he has been a Professor at the School of Statistics and Data Science at Southeast University.
His primary research areas include biostatistics, statistical genetics, high-dimensional compositional data analysis, and kernel methods. He has published multiple articles in major statistics, biostatistics and bioinformatics journals such as JASA, Annals of Applied Statistics, Biometrics, and Bioinformatics.For further information, please feel free to contact Dr. Zhan via email.
Contact Info:
Email：zhanx@seu.edu.cn
PI & Co-I:
1. NIH(R21AI144765), Novel statistical methods for controlled variable selection of microbiome data，2020-2021(Principle Investigator)
2. NSF(DMS1953189), Collaborative Research: New Methods, Theory and Applications for Nonsmooth Manifold-Based Learning，2020-2021(Former Co-Principal Investigator)
3. NIH(U01DK127384), Data Coordinating Center for the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC)，2020-2021(Investigator)
Selected Papers (*corresponding author):
1. Jiang, R., Zhan, X.*, & Wang, T.*, 2023. A flexible zero-inflated poisson-gamma model with application to microbiome sequence count data. Journal of the American Statistical Association, 118, 792-804.
2. Rios, N., Xue, L., & Zhan, X.*, 2024. A latent variable mixture model for composition-on-composition regression with application to chemical recycling. Annals of Applied Statistics, 18, 3253-3273.
3. Srinivasan, A., Xue, L.*, & Zhan, X.*, 2021. Compositional knockoff filter for high-dimensional regression analysis of microbiome data. Biometrics, 77, 984-995.
4. Zhan, X.*, Plantinga, A., Zhao, N., & Wu, M. C.*, 2017. A fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics, 73, 1453-1463.
5. Zhan, X., Tong, X., Zhao, N., Maity, A., Wu, M. C.*, & Chen, J.*, 2017. A small‐sample multivariate kernel machine test for microbiome association studies. Genetic Epidemiology, 41, 210-220.
