Changbo Zhu
Assistant Professor
Department of Applied and Computational Mathematics and Statistics
Fellow of Lucy Family Institute for Data & Society
University of Notre Dame
Office: 101H Crowley Hall, Notre Dame, IN 46556
Email: czhu4@nd.edu
Research interests: distributional and object data analysis; time series analysis; functional data analysis; clustering analysis; longitudinal data analysis; optimal transport; distance covariance; HSIC; change point detection; high dimensional statistics.
Education and Training
Postdoc University of California, Davis 2020 Oct - 2022 Aug
Ph.D. University of Illinois at Urbana-Champaign 2016 Aug - 2020 Aug
MS National University of Singapore 2014 Aug - 2016 Jul
BS National University of Singapore 2010 Aug - 2014 Jul
Students
Kaheon Kim
PhD candidate in ACMS
Office: 206 Crowley Hall, Notre Dame, IN 46556
Email: kkim26@nd.edu
Working on optimal transport and its applications in statistics.
Publications and Preprints
name* indicates equal contribution; name indicates the corresponding author.
Zhu, C. and Müller H.-G. (2024+) Geodesic optimal transport regression. arXiv:2312.15376.
Jiang, F., Zhu, C. and Shao, X. (2024) Two-sample and change-point inference for non-Euclidean valued time series. Electronic Journal of Statistics 18(1): 848-894.
Zhu, C. and Müller H.-G. (2023) Autoregressive optimal transport models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 85(3): 1012-1033. [code]
Zhu, C., Chen, Y., Müller, H.-G., Wang, J.-L., O’Muircheartaigh, J., Bruchhage, M., Deoni, S. (2023) Trajectories of brain volumes in young children are associated with maternal education. Human Brain Mapping 44(8): 3168-3179.
Zhu, C. and Wang J.-L. (2023) Testing homogeneity: The trouble with sparse functional data. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 85(3): 705-731. [code]
Zhou, Y., Müller, H.-G., Zhu, C., Chen, Y., Wang, J.-L., O’Muircheartaigh, J., Bruchhage, M., Deoni, S. and RESONANCE Consortium (2023) Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother’s education. Scientific Reports 13(1): 2984.
Zhu, C. and Müller H.-G. (2023) Spherical autoregressive models, with application to distributional and compositional time series. Journal of Econometrics. [code]
Wang, R.*, Zhu, C.*, Volgushev, S. and Shao, X. (2022) Inference for change-points in high dimensional data via self-normalization. The Annals of Statistics 50(2): 781-806. [code]
Zhu, C. and Shao, X. (2021) Interpoint distance based two sample tests in high dimension. Bernoulli 27(2): 1189-1211.
Zhu, C., Zhang, X., Yao, S., and Shao, X. (2020) Distance-based and RKHS-based dependence metrics in high dimension. The Annals of Statistics 48(6): 3366-3394.
Lu, C., Zhu, C., Xu, C., Yan, S., and Lin, Z. (2015) Generalized singular value thresholding. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 29(1).
Zhu, C., Xu, H., Leng, C., and Yan, S. (2014) Convex optimization procedure for clustering: Theoretical revisit. Advances in Neural Information Processing Systems (NeurIPS) 27: 1619-1627.
Teaching
Fall 2023 ACMS 40878 - Computational Statistics with R
Fall 2023 ACMS 60888 - Statistical Computing and Monte Carlo Methods
Spring 2023 ACMS 60888 - Statistical Computing and Monte Carlo Methods
Fall 2022 ACMS 40875 - Statistical Methods in Data Mining and Prediction
Activities
Invited talk, 2023 ICSA Applied Statistics Symposium "Testing homogeneity: The trouble with sparse functional data".
Invited talk, EcoSta 2023 "Testing homogeneity: The trouble with sparse functional data".
Contributed talk, JSM 2021 "Autoregressive Optimal Transport Models".
Session chair, JSM 2021 "Recent Developments in Statistical Inference Using Distance Correlation and Related Dependence Metrics".
Contributed talk, JSM 2020 "Distance-based and RKHS-based Dependence Metrics in High-dimension".
Contributed talk, JSM 2019 "Dissimilarity Metrics Based Two Sample Tests in High Dimension".
Poster presentation, COSDA 2019 "Dissimilarity Metrics Based Two Sample Tests in High Dimension".