Faculty Profile

Yanming  Li, PhD, M.S.

Yanming Li, PhD, M.S.

  • Research Investigator
  • M4011, SPH II 
  • 1415 Washington Heights
  • Ann Arbor, MI 48109-2029

Yanming Li received his PhD in Biostatistics from the University of Michigan in 2014. He was a postdoctoral research fellow at the Kidney Epidemiology and Cost Center, University of Michigan from 2015-2017 and a research investigator at Biostatistics department, University of Michigan from 2018-current.

  • PhD, Biostatistics, University of Michigan, 2014
  • M.S., Statistics, Michigan State University, 2008

My research interests include high-dimensional variable selection with application to cancer-genomics, neuroimaging studies; Developing statistical methods, computational algorithms for analyzing big-volume and complex structured data such as electronic health record and health service data; Weak signal detection, estimation and their effects in prediction; Survival analysis with high-dimensional predictors, interval censored data, correlation estimation for censored data; Graphical models, hidden Markov models.

I have been involved in the projects such as End Stage Renal Disease (ESRD) Data Utilization, Measure & Instrument Development and Support (MIDS) and Comprehensive End-Stage Renal Disease (ESRD) Care (CEC) at UM-KECC.


  • Li, Y., Hong, H. G. and Li, Y. (2019). Multiclass linear discriminant analysis with ultrahigh-dimensional features. Biometrics. DOI:10.1111/biom.13065.

  • Li, Y., Hong, H. G., Ahmed, S. E. and Li, Y. (2019). Weak signals in high-dimension regression: detection, estimation. Applied Stochastic Models in Business and Industry. 35, 283-298. DOI:10.1002/asmb.2340.

  • Li, Y., Gillespie, B., Shedden, K. and Gillespie, J. (2018). Calculating profile likelihood estimates of the correlation coefficient in the presence of left, right or interval censoring and missing data. The R Journal . 10:2, 159-179. DOI:10.32614/RJ-2018-040.

  • He, K., Kang, J., Hong, H. G., Zhu, J., Li, Y., Lin, H., Xu, H., Li, Y. (2018) Covariance-insured screening. Computational Statistics and Data Analysis. 132, 100-114. DOI:10.1016/j.csda.2018.09.001.

  • Kalbfleisch, J., He, K., Lu, X. and Li, Y. (2018). Does the Inter-Unit Reliability (IUR) Measure Reliability. Health Services and Outcomes Research Methodology. 18(3) 215-225. DOI:10.1007/s10742-018-0185-4.

  • Li, Y., Hong, H. G., Li, Y. (2017) Discussion of the paper Post Selection Shrinkage Estimation for High Dimensional Data Analysis. Applied Stochastic Models in Business and Industry, 33, 126-129.

  • Zhou, X. J., Nath, S. K, Qi, Y. Y., Sun, C., Hou, P., Zhang, Y. M., Lv, J. C., Shi, S. F., Liu, L. J., Chen, R. Y., Yang, W. L., He, K., Li, Y. and Zhang, H. (2016). Novel identified associations of RGS1 and RASGRP1 variants in IgA Nephropathy. Scientific Reports, 6. DOI:10.1038/srep35781.

  • He, K., Li, Y., Zhu, J., Liu, H., Lee, J., Amos, C., Hyslop, T., Jin, J., Lin, H., Wei, Q. and Li, Y. (2016) Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates. Bioinformatics, 32, 50-57.

  • Li, Y., Nan, B. and Zhu, J. (2015). Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics, 71 (2), 354-363.

  • Tsoi, L. S., Spain, S., Knight, J., Ellinghaus, E., Stuart, P., Capon, F., Ding, J., Li, Y., Tejasvi, T., Gudjonsson, J., Kang, H. M., Allen, M., McManus, R., Novelli, G., Samuelsson, L., Schalkwijk, J., Ståhle, M., Burden, A., Smith, C., Cork, M., Estivill, X., Bowcock, A., Krueger, G., Weger, W., Worthington, J., Tazi-Ahnini, R., Nestle, F., Hayday, A., Hoffmann, P., Winkelmann, J., Wijmenga, C., Langford, C., Edkins, S., Andrews, R., Blackburn, H., Strange, A., Band, G., Pearson, R., Vukcevic, D., Spencer, C., Deloukas, P., Mrowietz, U., Schreiber, S., Weidinger, S., Koks, S., Kingo, K., Esko, T., Metspalu, A., Lim, H., Voorhees, J., Weichenthal, M., Chandran, V., Rosen, C., Rahman, P., Gladman, D., Griffiths, C., Reis, A., Kere, J., Nair, R., Franke, A., Barker, J., Abecasis, G. R., Elder, J. T. and Trembath, R. (2012). Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nature Genetics, 44, 1341-1348.

  • American Statistical Association (ASA)
  • American Society of Human Genetics (ASHG)
  • Eastern North American Region of the International Biometric Society (ENAR)
  • Lifetime Data Analysis Interest Group (LIDA-IG)