Faculty Profile

Zhi  He, PhD

Zhi He, PhD

  • Research Associate Professor of Biostatistics

Department of Biostatistics
University of Michigan
1415 Washington Heights
Suite 3645, Room 3655, SPH I
Ann Arbor, Michigan 48109

Kevin He is a Research Associate Professor in the Department of Biostatistics and is a core faculty member of the Kidney Epidemiology and Cost Center (KECC) at the University of Michigan. He received his PhD in Biostatistics from the University of Michigan in 2012.  His primary research interests are in survival analysis, healthcare provider profiling, data integration and statistical optimization with application in national disease registries, transplantation, kidney dialysis and genetics.

  • Ph.D. in Biostatistics, University of Michigan, 2012
  • M.S. in Biostatistics, University of Michigan, 2008
  • B.S. in Statistics, Queen’s University, 2006
  • M.S. in Epidemiology, Queen’s University, 2004
  • B.M. in Clinical Medicine, Dalian Medical University, 2002

Survival Analysis, Healthcare Provider Profiling,  Machine Learning, Statistical Optimization for Big Data Analysis, Data Integration, Kidney Dialysis, Transplantation, Causal Inference and Statistical Genetics.

  • Wang, D., Ye, W. and He, K. (2021).  Kullback-Leibler-based discrete relative risk models for integration of published prediction models with new dataset. Proceedings of Machine Learning Research.
  • He, K., Kang, J., Zhu, J. and Li, Y. (2021).  Stratified Cox models with time-varying effects for national kidney transplant patients:  a new block-wise steepest ascent method. Biometrics, In Press.
  •  He, K., Dahlerus, C., Xia, L., Li, Y.M. and Kalbfleisch, J.D. (2020).  The profiling inter-unit reliability. Biometrics, 76(2), 654-663.
  •  He, K., Li, Y., Rao, P.S., Sung, R.S. and Schaubel, D.E. (2020).  Prognostic score matching methods for estimating the average treatment effect on the survival function when treatment is time-dependent. Lifetime Data Analysis, 26(3), 451-470.
  •  Jiang, H. and He, K. (2020).  Statistics in the genomic era. Genes, In Press.
  •  He, K., Wang, Y., Zhou, X., Xu, H. and Huang, C. (2019).  An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis. Lifetime Data Analysis, 25(3), 569-585.
  •  He, K., Ashby, V.B. and Schaubel, D.E. (2019).  Evaluating center-specific long term outcomes through differences in mean survival time. Statistics in Medicine, 38(11),1957-1967.
  •  He, K., Kalbfleisch, J.D., Yang, Y. and Fei, Z. (2019).  Inter-unit reliability for nonlinear models. Statistics in Medicine, 38(5), 844-854.
  • He, K., Kang, J., Hong, H.G., Zhu, J., Li, Y.M., Lin, H.Z., Xu, H. and Li, Y. (2019). Covariance-insured screening. Computational Statistics & Data Analysis, 132,100-114.
  • He, K., Zhou, X., Jiang, H., Wen, X.Q. and Li, Y. (2018).  False discovery control for penalized variable selections with high-dimensional covariates. Statistical Applications in Genetics and Molecular Biology, 17(6).
  • He, K., Yang, Y., Li, Y.M., Zhu, J. and Li, Y. (2017).  Modeling time-varying effects with large-scale survival data:  an efficient quasi-Newton approach. Journal of Computational and Graphical Statistics, 26(3), 635-645.
  • He, K., Li, Y.M., Zhu, J., Liu, H.L., Lee, J.E., Amos, C.I., Hyslop, T., Jin, J.S., Lin,H.Z., Wei, Q.Y. and Li, Y. (2016).  Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates. Bioinformatics, 32(1), 50-57.
  • He, K. and Schaubel, D.E. (2015).  Standardized mortality ratio for evaluating center-specific mortality:  assessment and alternative. Statistics in Biosciences, 7,296-321.
  • He, K. and Schaubel, D.E. (2014).  Semiparametric methods for relative risk center effect measures. Lifetime Data Analysis, 20(4), 619-644.
  • He, K. and Schaubel, D.E. (2014).  Methods for estimating center effects in survival analysis using direct standardization. Statistics in Medicine, 33(12), 2048-2061.
  • He, K., Kalbfleisch, J.D., Li, Y. and Li, Y.J. (2013).  Evaluating hospital readmission rates in dialysis facilities; adjusting for hospital effects. Lifetime Data Analysis, 19(4), 490-512.