Zhen Qin
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Zhen Qin
University of Michigan
Michigan Institute for Computational Discovery and Engineering (MICDE)
Department of Electrical Engineering and Computer Science
Department of Statistic

Email: zhenqin@umich.edu, qin.660@osu.edu

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Zhen Qin

Hello, I am Zhen Qin, a MICDE Postdoctoral Fellow jointly appointed in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of Michigan. I am fortunate to be mentored by Professors Qing Qu and Yang Chen. I received my Ph.D. in Computer Science and Engineering from The Ohio State University, where I was privileged to be advised by Professor Zhihui Zhu. My current research encompasses several intriguing domains:

  • Theoretical understanding of complex models in tensor learning, signal processing, communication, quantum tomography and machine learning.

  • Data processing leveraging special structures in high-dimensional space including sparse, low-rank, manifold-based and tensor networks-based models.

Research Overview: My research focuses on the design and analysis of models and algorithms in scientific and engineering domains, leveraging numerical optimization, mathematical signal processing, and information theory. In particular, I seek theoretical insights into EE/CS models arising in quantum information, communication systems, and neural network architectures. By deepening theoretical insights, I aim to further enhance and refine practical models, bridging the gap between theory and real-world applications. alt text

News and Updates

  • [Aug 2025] Our paper has been accepted at npj Quantum Information, presenting a projected classical shadow method for quantum state tomography.

  • [Jul 2025] Our paper has been accepted at TPAMI, offering computational and statistical guarantees for tensor-on-tensor regression with tensor train decomposition.

  • [Jun 2025] Our paper has been released at arXiv, proposing a scalable factorization approach for high-order structured tensor recovery.

  • [Apr 2025] Our paper has been accepted at TSP, offering a theoretical analysis of the robust tensor train (TT) recovery problem and demonstrating that TT-format tensors can be robustly recovered even when up to half of the measurements are arbitrarily corrupted.

  • [Apr 2025] Honored to receive the 2025 CSE Graduate Research Award at Ohio State.

  • [Dec 2024] Our paper has been accepted at JMLR, offering a convergence guarantee for the factorization approach in arbitrary-order tensor train recovery.

  • [Dec 2024] Our paper has been released at arXiv, providing optimal error analysis of channel estimation for IRS-assisted MIMO systems.

  • [Nov 2024] Our paper has been released at arXiv, investigating the optimal allocation of Pauli measurements in the low-rank quantum state tomography.

  • [Oct 2024] Our paper has been released at arXiv, proving that a linear number of state copies is required to guarantee bounded recovery error of an matrix product operator state in the quantum state tomography, thereby improving the theoretical result in our TIT paper.

  • [Mar 2024] Our paper has been accepted at SPL, analyzing the linear converence rate of training the orthonormal deep linear neural networks.

  • [Jan 2024] Our paper has been accepted at TIT, demonstrating that a polynomial number of state copies is required to guarantee bounded recovery error of an matrix product operator state in the quantum state tomography.

  • [Jan 2024] A series of proportionate recursive least squares (PRLS) algorithms have been completed and accepted in the following papers: paper: PRLS, paper: L1-PRLS, paper: VSS-CR-PRLS and paper: Fast PRLS, exploring proportionate sparsity in the adaptive signal processing during my master's studies.

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