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
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My research explores the interplay between statistics, geometry, and optimization.
Interested topics: optimal transport; functional and object data analysis; time series analysis; high dimensional statistics; distance covariance/HSIC; longitudinal data analysis; change point detection; applications in understanding brain development.
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
Students
Kaheon Kim
PhD candidate in ACMS
Office: 206 Crowley Hall, Notre Dame, IN 46556
Email: kkim26@nd.edu
Working on statistical optimal transport: computation and modeling.
Publications and Preprints
name* indicates students supervised; name indicates the corresponding author.
Kim, K.*, Zhou, B., Zhu, C. and Chen, X. (2025+) Sobolev gradient ascent for optimal transport: barycenter optimization and convergence analysis. arXiv:2505.13660.
Zhu, C. and Müller H.-G. (2025+) Geodesic optimal transport regression. arXiv:2312.15376.
Zhang, Y., Zhu, C. and Shao, X. (2025) Change-point detection for object-valued time series. Journal of Business & Economic Statistics. In press.
Kim, K.*, Yao, R., Zhu, C. and Chen, X. (2025) Optimal transport barycenter via nonconvex-concave minimax optimization. International Conference on Machine Learning (ICML). [Python]
Zhu, C., Yao, J. and Wang, J.-L. (2024) Testing independence for sparse longitudinal data. Biometrika 111(4): 1187-1199.
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. (2024) Spherical autoregressive models, with application to distributional and compositional time series. Journal of Econometrics 239(2): 105389. [R]
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. [R]
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. [R]
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.
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. Wang and Zhu contributed equally to this work [R]
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
ACMS 60880 - Optimization Algorithms for Machine Learning
ACMS 40878 - Computational Statistics with R
ACMS 60888 - Statistical Computing and Monte Carlo Methods
ACMS 40875 - Statistical Methods in Data Mining and Prediction
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