Jiehuan Sun
Associate Professor
Epidemiology and Biostatistics
Contact
Building & Room:
986 SPHPI
Address:
1603 W. Taylor St.
Office Phone:
Email:
About
Jiehuan Sun’s primary research interests are to develop novel statistical methods to extract meaningful insights from large-scale, complex biomedical data, particularly electronic health records and genomics data. His work aims to accelerate the translation of discoveries from bench to bedside, supporting advances in personalized medicine and individualized healthcare. His methodological development centers on longitudinal data analysis, survival data analysis, high-dimensional data analysis, and joint modeling with applications spanning risk prediction, phenotyping, network inference, biomarker discovery, and clustering. He also actively collaborates with biomedical researchers across a broad range of disease areas.
Selected Publications
Lee, Kuang-Yao, Jiehuan Sun, Bing Li, and Lexin Li (2026). Generalized point process additive models. Journal of the Royal Statistical Society Series B: Statistical Methodology: qkag061.
Jiehuan Sun (2025). Dynamic prediction with penalized joint frailty model of high-dimensional recurrent event data and a survival outcome. The Annals of Applied Statistics 19, no. 4 : 3203-3220.
Jiehuan Sun and Sanjib Basu (2024). Penalized Joint Models of High-Dimensional Longitudinal Biomarkers and A Survival Outcome. The Annals of Applied Statistics, 18, no. 2: 1490-1505.
Jiehuan Sun, Katherine P. Liao, and Tianxi Cai (2024). Learning Healthcare Delivery Network with Longitudinal Electronic Health Records Data. The Annals of Applied Statistics, 18, no. 1: 882-898.
Jiehuan Sun and Kuang-Yao Lee (2024). Generalized Functional Linear Model with A Point Process Predictor. Statistics in Medicine 43, no. 8 : 1564-1576.
Yi-Han Sheu, Jiehuan Sun, Hyunjoon Lee, Victor M. Castro, Yuval Barak-Corren, Eugene Song, Emily Madsen, William J. Gordon, Isaac S. Kohane, Susanne E. Churchill, Ben Reis, Tianxi Cai, Jordan W. Smoller (2023). An Efficient Landmark Model for Prediction of Suicide Attempts in Multiple Clinical Settings. Psychiatry Research 323, 115175.
Jieqi Tu and Jiehuan Sun (2023). Gaussian variational approximate inference for joint models of longitudinal biomarkers and a survival outcome. Statistics in Medicine 42 no. 3 : 316-330.
Katherine P. Liao, Jiehuan Sun, Tianrun A. Cai, Nicholas Link, Chuan Hong, Jie Huang, Jennifer Huffman, Jessica Gronsbell, Yichi Zhang, Yuk-Lam Ho, Jacqueline Honerlaw, Lauren Costa, Victor Castro, Vivian Gainer, Shawn Murphy, Christopher J. O’Donnell, J. Michael Gaziano, Kelly Cho, Peter Szolovits, Isaac Kohane, Sheng Yu, Tianxi Cai, with the VA Million Veteran Program (2019). High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS. Journal of the American Medical Informatics Association, 26(11), 1255-1262.
Jiehuan Sun, Jose D. Herazo-Maya, Philip L. Molyneaux, Toby M. Maher, Naftali Kaminski, and Hongyu Zhao (2019). Regularized latent class model for joint analysis of high dimensional longitudinal biomarkers and a time-to-event outcome. Biometrics, 75(1), 69-77.
Jiehuan Sun, Jose D. Herazo-Maya, Naftali Kaminski, Hongyu Zhao, and Joshua L. Warren (2017). A Dirichlet process mixture model for clustering longitudinal gene expression data. Statistics in Medicine, 36(22), 3495-3506.