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Representative Publications

  • Zhang, K., Zhao, Z. and Zhou, W., 2021. Beauty powered beast. arXiv preprint arXiv:2103.00674.
  • Zhang, K., 2019. BET on Independence. Journal of the American Statistical Association114(528), pp.1620-1637.
  • Huang, J., Fang, F., Turkiyyah, G., Cao, J., Genton, M.G. and Keyes, D.E., 2018. O (N) Hierarchical algorithm for computing the expectations of truncated multi-variate normal distributions in N dimensions. arXiv preprint arXiv:1809.08315.
  • Zheng, C., Huang, J., Wood, I.A. and Wu, Y., 2022. A modified expectation‐maximization algorithm for latent Gaussian graphical model. Canadian Journal of Statistics50(2), pp.612-637.
  • Dym, N. and Kovalsky, S.Z., 2019. Linearly converging quasi branch and bound algorithms for global rigid registration. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1628-1636).
  • Shtengel, A., Poranne, R., Sorkine-Hornung, O., Kovalsky, S.Z. and Lipman, Y., 2017. Geometric optimization via composite majorization. ACM Trans. Graph.36(4), pp.38-1.
  • Li, Y., Min, M.R., Lee, T., Yu, W., Kruus, E., Wang, W. and Hsieh, C.J., 2021. Towards Robustness of Deep Neural Networks via Regularization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 7496-7505).
  • Li, Y., Cheng, M., Fujii, K., Hsieh, F. and Hsieh, C.J., 2018. Learning from group comparisons: exploiting higher order interactions. Advances in Neural Information Processing Systems31.
  • Berkolaiko, G., Cox, G. and Marzuola, J.L., 2019. Nodal deficiency, spectral flow, and the Dirichlet-to-Neumann map. Letters in Mathematical Physics109(7), pp.1611-1623.
  • Beck, T., Bors, I., Conte, G., Cox, G. and Marzuola, J.L., 2021. Limiting eigenfunctions of Sturm–Liouville operators subject to a spectral flow. Annales mathématiques du Québec45(2), pp.249-269.
  • Tucker, D.C., Wu, Y. and Müller, H.G., 2021. Variable Selection for Global Fréchet Regression. Journal of the American Statistical Association, pp.1-15.
  • Jun Shin, S., Wu, Y. and Hao, N., 2020. A backward procedure for change‐point detection with applications to copy number variation detection. Canadian Journal of Statistics48(3), pp.366-385.
  • Santo, S. and Zhong, P.S., 2020. Homogeneity Tests of Covariance and Change-Points Identification for High-Dimensional Functional Data. arXiv preprint arXiv:2005.01895.
  • Li, J. and Zhong, P.S., 2017. A rate optimal procedure for recovering sparse differences between high-dimensional means under dependence. The Annals of Statistics45(2), pp.557-590.


Software Packages

  • O(N) Hierarchical algorithm for computing the expectations of truncated multi-variate normal distributions in N dimensions. Matlab codes. Tridiagonal case and Exponential Model Case codes are available. The Exponential Case code requires the NuFFT package developed by Greengard and Lee.

Presentation Lectures