Kun Liang

Associate Professor
Kun Liang

Contact Information:
Kun Liang

Kun Liang's personal website

Research interests

  • Large-scale inference

  • Statistical genetics

  • High-dimensional statistics

  • Machine learning


  • PhD, Iowa State University, U.S.A.
  • BE, TsingHua University, China

Selected publications

  • MacDonald, P.*, Liang, K., and Janssen, A. (2019), “Dynamic adaptive procedures that control the false discovery rate,” Electronic Journal of Statistics, 13, 3009–3024.

  • Liang, K., Du, C., You, H.*, and Nettleton, D. (2018), “A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets,” BMC bioinformatics, 19.

  • Nie, Z.*, and Liang, K. (2017), “Adaptive filtering increases power to detect differentially expressed genes,” in New advances in statistics and data science, Springer, pp. 127–136.

  • Liang, K. (2016), “False discovery rate estimation for large-scale homogeneous discrete p-values,” Biometrics, 72, 639–648.

  • Liang, K., and Keleş, S. (2012), “Detecting differential binding of transcription factors with ChIP-seq,” Bioinformatics, 28, 121–122.

  • Liang, K., and Keleş, S. (2012), “Normalization of ChIP-seq data with control,” BMC Bioinformatics, 13, 199.

  • Liang, K., and Nettleton, D. (2012), “Adaptive and dynamic adaptive procedures for false discovery rate control and estimation,” Journal of the Royal Statistical Society, Series B, 74, 163–182.

  • Liang, K., and Nettleton, D. (2010), “A hidden Markov model approach to testing multiple hypotheses on a tree-transformed Gene Ontology graph,” Journal of the American Statistical Association, 105, 1444–1454.