Preprints

• Accommodating multiple potential normalizations in microbiome associations studies.
  Song, H., Ling, W., Zhao, N., Plantinga, A.M., Broedlow, C.A., Klatt, N.R., Hensley-McBain, T., Wu, M.C.

• New graph-based multi-sample tests for high-dimensional and non-Euclidean data.
  Song, H., Chen, H.   [R package: gTestsMulti]

• New kernel-based change-point detection.
  Song, H., Chen, H.   [R package: kerSeg]

• A fast kernel independence test for cluster-correlated data.
  Song, H., Liu, H., Wu, M.C.

• A fast and effective large-scale two-sample test based on kernels.
  Song, H., Chen, H.

• Generalized kernel two-sample tests.
  Song, H., Chen, H.   [R package: kerTests]

Publications

• Batch effects removal for microbiome data via conditional quantile regression.
  Ling, W., Lu, J., Zhao, N., Lulla, A., Plantinga, A., Fu, W., Zhang, A., Liu, H., Song, H., Li, Z., Chen, J.,
  Randolph, T., Koay, W., White, J., Launer, L., Fodor, A., Meyer, K., Wu, M.C. (2022).
  Nature Communications, 13(1):1-14.

• Asymptotic distribution-free change-point detection for data with repeated observations.
  Song, H., Chen, H. (2022).   [R package: gSeg]
  Biometrika, 109(3):783-798.

• Preventing failures by dataset shift detection in safety-critical graph applications.
  Song, H., Thiagarajan, J., Kailkhura, B. (2021).
  Frontiers in Artificial Intelligence, 4:589632.