Peter X. K. Song, PhD

• PhD, University of British Columbia, Vancouver, 1996
• BS, Jilin University, Changchun, 1985

Research Interests:
Statistical Foundation of Big Data Analytics, Longitudinal Data Analysis, Statistical Computing, Mediation Analysis,  Spatial/Spatiotemporal Data Analysis. Children Health, Chronic Disease, Environmental Health, Nephrology, Nutrition, Organ Exchange Programs, Smart Health.

Research Projects:
Song develops statistical methods and algorithms to discover high-dimensional causal mediation pathways of omics biomarkers that enable scientists to evaluate and validate how social and environmental determinants affect human growth and development.

Song develops machine learning algorithms to detect digital features predictive of health outcome using data generated from wearable devices and customized sensors with applications in reproductive health, sleep health and chronic disease management. 

Song develops statistical methods of homogeneity pursuit in regression analysis, including statistical theory, integer optimization, and algorithms to help practitioners to understand the influence of environmental mixtures in health outcomes.

Song develops optimal algorithms to create optimal organ matching strategies in the kidney paired donation program to increase the quantity and quality of organ donation in kidney transplantation.

Song develops statistical methods, distributed inference, algorithms and software in big data integration and computation.

Chan, LS, Li, G, Fauman, EB, Yin, X, Lasskso, M, Boehnke, M and Song, PXK (2025). DrFARM: Identification and inference for pleiotropic gene in GWAS. Nature Communications 16:5789.

Zhang, L, Purkayastha, S, Lev-Tov, H, Nie, R, Kirsner, R, Cathie Spino, C and Song, PXK (2025). Determinants of enrolment rate in 397 clinical trials for healing diabetic foot ulcers: a systematic review. BMJ Open 15(7): e095512. 

Wang, W, Wu, S, Zhu, Z, Zhou, L and Song, PXK (2024).  Supervised homogeneity fusion: A combinatorial approach.  Annals of Statistics 52(1), 285-310.  

He, Y, Song, PXK and Xu, G (2024).  Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.  Journal of the Royal Statistical Society Series B 86, 411-434.  

Banker, MM, Zhang, L and Song, PXK (2024). Regularized scalar-on-function regression analysis to assess functional association of critical physical activity window with biological age.  Annals of Applied Statistics 18(4): 2730-2752. 

Sandrock, C and Song, PXK (2024). Limitation of Site-stratified Cox Regression Analysis in Survival Data: A Cautionary Tale of the PANAMO Phase III Randomized, Controlled Study in Critically Ill COVID-19 Patients. Trials 25:822. 

Patel, MR, Zhang, G, Heisler, M, Piette, JD, Resnicow K, Choe, H-M, Shi, X and Song, PXK (2024).  A randomized controlled trial to improve unmet social needs and clinical outcomes.  Journal of General Internal Medicine 39, 2415-2424. 

Luo, L, Zhou, L and Song, PXK (2023). Real-time regression analysis of streaming clustered data with possible abnormal data batches.  Journal of the American Statistical Association 118, 2029-2044.  

Banker, M and Song, PXK (2023).  Supervised learning of physical activity features from functional accelerometer data.  IEEE Journal of Biomedical and Health Informatics 27(12), 5710-5721. 

Shi, L, Wank, M, Chen, Y, Wang, Y, Hector, EC and Song, PXK (2022). Sleep classification with artificial synthetic imaging data using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics 27, 421- 432.

M4140 SPH II
1415 Washington Heights
Ann Arbor, MI 48109

Email: pxsong@umich.edu
Office: 734-764-9328

For media inquiries: sph.media@umich.edu