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Zhihui Zhu
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We are seeking Ph.D. students in Machine Learning (particularly analyses and practical techniques for deep learning, generative models, and LLMs), Signal Processing, and Quantum Information and Computing. Welcome candidates from any background, e.g., EECS, math, physics.
Call for papers: Conference on the Mathematical Theory of Deep Neural Networks, November 6-7, 2025 at Universtiy of Michigan in Ann Arbor
Recent Selected Publications:
Representation and Interpretability
J. Jiang, Y. Dong, J. Zhou, and Z. Zhu ‘‘From Compression to Expansion: A Layerwise Analysis of In-Context Learning," 2025.
J. Jiang, J. Zhou, Z. Zhu, ‘‘Tracing Representation Progression: Analyzing and Enhancing Layer-Wise Similarity," International Conference on Learning Representations (ICLR), 2025.
J. Jiang*, J. Zhou*, P. Wang, Q. Qu, D. Mixon, C. You, and Z. Zhu, ‘‘Generalized neural collapse for a large number of classes," International Conference in Machine Learning (ICML), 2024.
Quantum Information
Z. Qin, J. M. Lukens, B. T. Kirby, and Z. Zhu, ‘‘Enhancing Quantum State Reconstruction with Structured Classical Shadows," npj Quantum Information, 2025.
Z. Qin, C. Jameson, Z. Gong, M. B. Wakin, and Z. Zhu, ‘‘Quantum State Tomography for Matrix Product Density Operators," IEEE Transactions on Information Theory, vol. 70, pp. 5030 - 5056, 2024.
Z. Zhu, J. Lukens, and B. Kirby, ‘‘On the connection between least squares, regularization, and classical shadows," Quantum, vol. 8, 2024.
News:
[Aug 2025] Zhen defended his PhD thesis and will join University of Michigan as MICDE Postdoctoral Fellow. His PhD work received graduate research award from CSE of OSU.
[June 2024] Gave a tutorial on “Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice” at CVPR 2024.
[May 2024] Thrilled to receive the ORAU Ralph E. Powe Junior Faculty Enhancement Award.
[April 2024] Tutorial on “Understanding Deep Representation Learning via Neural Collapse” at ICASSP’24 with Prof. Laura Balzano, Dr. Peng Wang, and Prof. Qing Qu. Slides: Lecture-1, Lecture-2
[Jun 2023] Gave a short course on ‘‘Learning Nonlinear and Deep Low-Dimensional Representations from High-Dimensional Data: From Theory to Practice" at ICASSP 2023.
[Jan 2023] Co-organized and gave a tutorail at the 3rd SLowDNN Workshop at MBZUAI, Abu Dhabi, MBZUAI.
[Dec 2022] Received CQISE Partnership Seed Award (PSA) and will collaborate with Brian Kirby (ARL) on quantum network.
[May 2022] Gave a 10-hours short course (with Sam Buchanan, Yi Ma, Qing Qu, John Wright, and Yuqian Zhang) on ‘‘Low-Dimensional Models for High-Dimensional Data: From Linear to Nonlinear, Convex to Nonconvex, and Shallow to Deep" at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[Nov 2021] Co-organizing the 2nd workshop on ‘‘Seeking Low-dimensionality in Deep Neural Networks (SLowDNN)’’, Nov. 22nd – Nov. 23rd, 2021.
[Nov 2020] Co-organizing IEEE workshop on ‘‘Seeking Low-dimensionality in Deep Neural Networks (SLowDNN)’’, Nov. 23rd – Nov. 24th, 2020.