Faculty Profile

Zhi  He, PhD

Zhi He, PhD

  • Research Associate Professor
  • Suite 3645 SPH I, Room 3663
  • 1415 Washington Heights
  • Ann Arbor, Michigan 48109-2029

Kevin He is a Research Associate Professor of Biostatistics. He received his PhD in Biostatistics from the University of Michigan in 2012. He was a postdoctoral research fellow for the Kidney Epidemiology and Cost Center at the University of Michigan from 2012-1014.

  • PhD, Biostatistics, University of Michigan, 2012
  • MS., Biostatistics, University of Michigan, 2008
  • BS., Statistics, Queen's University, Canada, 2006
  • MS., Epidemiology, Queen's University, Canada, 2004
  • BM., Clinical Medicine, Dalian Medical University, China, 2002

  • My research interests include survival analysis, high-dimensional data analysis, statistical genetics, statistical methods for epidemiology and causal inference. I have also been working on statistical methods for analyzing large-scale data arising from CKD and ESRD studies.

  • He, K., Ashby, V.B., and Schaubel, D.E. (2018). Evaluating center-specific long term outcomes through differences in mean survival time. Statistics in Medicine, In Press.
  • He, K., Kang, J., Hyokyoung, G.H., Li, Y.M., Zhu, J., Lin, H.Z., Xu, H. and Li, Y.(2018). Covariance-insured screening. Computational Statistics & Data Analysis, In Press.
  • 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, In Press.
  • He, K., Yang, Y., Fei, Z. and Kalbfleisch, J.D. (2018). Inter-unit reliability for evaluating health care providers. Statistics in Medicine, In Press.
  • He, K., Wang, Y., Xu, H., Huang, C. and Zhou, X. (2018). An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis. Lifetime Data Analysis, In Press.
  • He, K., Xu, H. and Kang, J. (2018). A selective overview of feature screening methods with applications to neuroimaging data. WIRES Computational Statistics, In Press.
  • He, K., Yang, Y., Li, Y.M., Zhu, J. and Li, Y. (2016). 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. (2015). 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. (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. and Schaubel, D.E. (2014). Standardized mortality ratio for evaluating center-specic mortality: assessment and alternative. Statistics in Bioscience, 7(2):296-321.
  • 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.