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, Spatial/Spatiotemporal Data Analysis. Children Health, Chronic Disease, Environmental Health, Nephrology, Nutrition, Organ Exchange Programs, Smart Health.

Research Projects:
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 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 statistical methods, distributed inference, algorithms and software in big data integration and computation.

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 (to appear).

Zhou, L, Sun, Q, Fu, H and Song, PXK (2022). Subgroup-effects models for the analysis of personal treatment effects. Annals of Applied Statistics 16, 80-103.

Wang, F, Zhou, L, Tang, L and Song, PXK (2021). Method of contraction-expansion for simultaneous inference in linear model. Journal of Machine Learning Research 22(192), 1-32.

Tang, L, Zhou, Y, Wang, L, Purkayastha, S, Zhang, L, He, J, Wang, F and Song, PXK (2020). A review of multi-compartment infectious disease models. International Statistical Review 88(2), 462-513.

Hector, EC and Song, PXK (2021). A distributed and integrated method of moments for high-dimensional correlated data analysis [This paper received the 2017 ENAR John Van Ryzin Award]. Journal of the American Statistical Association 116 (534), 805-818.

Luo, L and Song, PXK (2020). Renewable estimation and incremental inference in generalized linear models with streaming datasets. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82, 69-97.

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