(Photo by SL, on the way to Albuquerque for SIAM IS16, May 2016)
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Preprints (* indicates equal contribution)

• Q. Qu, Y. Zhai, X. Li, Y. Zhang, and Z. Zhu, ‘‘Analysis of the Optimization Landscapes for Overcomplete Representation Learning,’’ preprint, 2019.

• X. Li*, S. Chen*, Z. Deng, Q. Qu, Z. Zhu, and A.M.-C. So, ‘‘Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods,’’ preprint, 2019.

• X. Li, Z. Zhu, A. M. So, and J. Lee, ‘‘Incremental Methods for Weakly Convex Optimization,’’ preprint, 2019.

• Q. Qu, X. Li, and Z. Zhu, ‘‘A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution,’’ preprint, 2019.

• Q. Li*, Z. Zhu*, G. Tang, and M. B. Wakin, ‘‘Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems,’’ preprint, 2019.

• Z. Zhu, Y. Wang, D. P. Robinson, D. Naiman, R. Vidal, and M. C. Tsakiris, ‘‘Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms,’’ preprint, 2018.

• Z. Zhu and X. Li, ‘‘Convergence Analysis of Alternating Nonconvex Projections,’’ preprint, 2018.

• Z. Zhu and M. B. Wakin, ‘‘Time-Limited Toeplitz Operators on Abelian Groups: Applications in Information Theory and Subspace Approximation,’’ preprint, 2017.

• Z. Zhu, Q. Li, G. Tang, and M. B. Wakin, ‘‘The Global Optimization Geometry of Nonsymmetric Matrix Factorization and Sensing,’’ preprint, 2017.

Journal Papers

• X. Li*, Z. Zhu*, A.M.-C. So, and R. Vidal, ‘‘Nonconvex Robust Low-rank Matrix Recovery,’’ to appear in SIAM Journal on Optimization, 2019.

• Z. Zhu, D. Soudry, Y. C. Eldar, and M. B. Wakin, ‘‘The Global Optimization Geometry of Shallow Linear Neural Networks,’’ Mathematical Foundations of Deep Learning in Imaging Science, special issue of Journal of Mathematical Imaging and Vision, pp. 1-14, 2019. (authors’ copy)

• S. Karnik, Z. Zhu, M. B. Wakin, J. Romberg, and M. A. Davenport, ‘‘The Fast Slepian Transform,’’ Applied and Computational Harmonic Analysis, vol 46, no. 3, pp. 624-652, May 2019. (authors’ copy)

• Q. Li, Z. Zhu, and G. Tang, ‘‘The Non-convex Geometry of Low-rank Matrix Optimization,’’Information and Inference: A Journal of the IMA, vol 8, no. 1, pp. 51-96, March 2019. (authors’ copy)

• T. Hong, X. Li, Z. Zhu, and Q. Li, ‘‘Optimized Structured Sparse Sensing Matrices for Compressive Sensing,’’ Signal Processing, vol. 159, pp. 119-129, June 2019.(authors’ copy)

• C. Wang, Z. Zhu, H. Gu, X. Wu, and S. Liu, ‘‘Hankel Low-rank Approximation for Seismic Noise Attenuation,’’ IEEE Transactions on Geoscience and Remote Sensing, vol 57, no. 1, pp. 561-573, January 2019.

• Z. Zhu, S. Karnik, M. B. Wakin, M. A. Davenport, and J. Romberg, ‘‘ROAST: Rapid Orthogonal Approximate Slepian Transform,’’ IEEE Transactions on Signal Processing, vol 66, no. 22, pp. 5887-5901, November 2018. (authors’ copy)

• Z. Zhu, Q. Li, G. Tang, and M. B. Wakin, ‘‘Global Optimality in Low-rank Matrix Optimization,’’ IEEE Transactions on Signal Processing, vol 66, no. 13, pp. 3614–3628, July 2018. (authors’ copy)

• Z. Zhu, G. Li, J. Ding. Q. Li, and X. He, ‘‘On Collaborative Compressive Sensing Systems: The Framework, Design and Algorithm,’’ SIAM Journal on Imaging Sciences, vol 11, no. 2, pp. 1717-1758, 2018. (authors’ copy)

• Z. Zhu, S. Karnik, M. A. Davenport, J. K. Romberg, and M. B. Wakin ‘‘The Eigenvalue Distribution of Discrete Periodic Time-Frequency Limiting Operators,’’ IEEE Signal Processing Letters, vol. 25, no. 1, pp.95–99, January 2018. (authors’ copy)

• Z. Zhu and M. B. Wakin, ‘‘Approximating Sampled Sinusoids and Multiband Signals Using Multiband Modulated DPSS Dictionaries,’’ Journal of Fourier Analysis and Applications, vol. 23, no. 6, pp. 1263-1310, December 2017. (authors’ copy)

• X. Wu, and Z. Zhu, ‘‘Methods to Enhance Seismic Faults and Construct Fault Surfaces,’’ Computers & Geosciences, vol. 107, pp. 37-48, October 2017.

• T. Hong and Z. Zhu, ‘‘An Efficient Method for Robust Projection Matrix Design,’’ Signal Processing, vol. 143, pp. 200-210, February 2018.(authors’ copy)

• Z. Zhu and M. B. Wakin, ‘‘On the Asymptotic Equivalence of Circulant and Toeplitz Matrices,’’ IEEE Transactions on Information Theory, vol. 63, no. 5, pp. 2975-2992, May, 2017. (author's copy)

• G. Li, Z. Zhu, D. Yang, L. Chang, and H. Bai, ‘‘On Projection Matrix Optimization for Compressive Sensing Systems,’’ IEEE Transactions on Signal Processing, vol. 61, no. 11, pp. 2887-2898, June 2013.

Conference Papers–Machine Learning

• Q. Qu, Y. Zhai, X. Li, Y. Zhang, and Z. Zhu, ‘‘Analysis of the Optimization Landscapes for Overcomplete Representation Learning,’’ to appear in International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. (authors’ copy) (oral)

• Z. Zhu, T. Ding, M.C. Tsakiris, D. Robinson, and R. Vidal, ‘‘A Linearly Convergent Method for Non-smooth Non-convex Optimization on Grassmannian with Applications to Robust Subspace and Dictionary Learning,’’ Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. (poster) (code)

• Z. Zhu*, Q. Li*, X. Yang, G. Tang, and M. B. Wakin, ‘‘Global Optimality in Distributed Low-rank Matrix Factorization,’’ Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019.

• Q. Qu, X. Li, and Z. Zhu, ‘‘A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution,’’ Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. (spotlight)

• Q. Li*, Z Zhu*, and G. Tang, ‘‘Alternating Minimizations Converge to Second-Order Optimal Solutions,’’ International Conference on Machine Learning (ICML), Long Beach, CA, USA, June 2019.

• T. Ding*, Z Zhu*, T. Ding, Y. Yang, D. Robinson, M. Tsakiris, and R. Vidal, ‘‘Noisy Dual Principal Component Pursuit,’’ International Conference on Machine Learning (ICML), Long Beach, CA, USA, June 2019.

• Z. Zhu, Y. Wang, D. P. Robinson, D. Naiman, R. Vidal, and M. C. Tsakiris, ‘‘Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms,’’ Neural Information Processing Systems (NeurIPS), Montreal, Quebec, Canada, December 2018. (author's copy)

• Z. Zhu*, X. Li*, K. Liu, and Q. Li, ‘‘Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization,’’ Neural Information Processing Systems (NeurIPS), Montreal, Quebec, Canada, December 2018. (author's copy) (poster)

Conference Papers–Signal Processing

• Q. Li, X. Yang, Z. Zhu, G. Tang, and M. B. Wakin, ‘‘The Geometric Effects of Distributing Constrained Nonconvex Optimization Problems,’’ IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019. (candidates for the Best Student Paper Award)

• Y. Li, Y. Zhang, and Zhihui Zhu, ‘‘Learning Deep Networks under Noisy Labels for Remote Sensing Image Scene Classification,’’ IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, July 2019.

• Q. Li, Z. Zhu, G. Tang, and M. B. Wakin, ‘‘The Geometry Of Equality-Constrained Global Consensus Problems,’’ IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019.

• Z. Zhu, M. Lopez-Santillana, and M. B. Wakin,‘‘Super-Resolution of Complex Exponentials from Modulations with Known Waveforms,’’ IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, December 2017.

• Z. Zhu, Q. Li, G. Tang, and M. B. Wakin, ‘‘Global Optimality in Low-rank Matrix Optimization,’’ IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, Quebec, Canada, November 2017.

• Z. Zhu, D. Yang, M. B. Wakin, and G. Tang, ‘‘A Super-resolution Algorithm for Multiband Signal Identification,’’ 51st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, October 2017.

• Z. Zhu, S. Karnik, M. B. Wakin, M. A. Davenport, and J. K. Romberg, ‘‘Fast Orthogonal Approximations of Sampled Sinusoids and Bandlimited Signals,’’ IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, March 2017.

• G. Li, Z. Zhu, H. Bai, and A. Yu, ‘‘A New Framework for Designing Incoherent Sparsifying Dictionaries,’’ IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, March 2017.

• Q. Li, S. Li, H. Mansour, M. Wakin, D. Yang, and Z. Zhu, ‘‘JAZZ: A Companion to MUSIC for Frequency Estimation with Missing Data,’’ IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, March 2017.

• S. Karnik, Z. Zhu, M. B. Wakin, J. K. Romberg, M. A. Davenport, ‘‘Fast Computations for Approximation and Compression in Slepian Spaces,’’ IEEE Global Conference on Signal and Information Processing (GlobalSIP), Greater Washington, D.C., December 2016.

• Z. Zhu and M. B. Wakin, ‘‘On the Dimensionality of Wall and Target Return Subspaces in Through-theWall Radar Imaging,’’ 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Aachen, Germany, September 2016.

• Z. Zhu, G. Tang, P. Setlur, S. Gogineni, M. Wakin, and M. Rangaswamy, ‘‘Super-Resolution in SAR Imaging: Analysis With the Atomic Norm,’’ IEEE Sensor Array and Multichannel Signal Processing (SAM) Workshop, Rio de Janeiro, Brazil, July 2016.

• Z. Zhu and M. B. Wakin, ‘‘New Analysis of Multiband Modulated DPSS Dictionaries,’’ Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS’15), Cambridge, England, July 2015.

• Z. Zhu and M. B. Wakin, ‘‘Wall Clutter Mitigation and Target Detection Using Discrete Prolate Spheroidal Sequences,’’ 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Pisa, Italy, June 2015.

• Z. Zhu and M. B. Wakin, ‘‘Detection of Stationary Targets Using Discrete Prolate Spheroidal Sequences,’’ International Review of Progress in Applied Computational Electromagnetics (ACES), Williamsburg, Virginia, March 2015.

• H. Bai, Z. Zhu, G. Li, and S. Li, ‘‘Design of Optimal Measurement Matrix for Compressive Detection,’’ Proc. of the 10th International Symposium on Wireless Communication Systems (ISWCS 2013), Ilmenau, Germany, August 2013.

• S. Li, Z. Zhu, G. Li, L. Chang, and Q. Li, ‘‘Projection Matrix Optimization for Block-sparse Compressive Sensing,’’ IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2014), Kunming, August 2013.

• Q. Li, Z. Zhu, S. Tang, L. Chang, and Gang Li, ‘‘Projection Matrix Optimization Based on SVD for Compressive Sensing Systems," 32nd Chinese Control Conference (CCC), Xi'an, July 2013.

• Z. Zhu, D. Yang, G. Li, and C. Huang, ‘‘Stable 2nd Order Adaptive IIR Filter Structure for Blind Deconvolution," Proc. 4th Int. Conf. on Image and Signal Processing (CISP), Shanghai, October 2011.

Ph.D. thesis

• Z. Zhu, ‘‘Subspace Approximation on the Continuum,’’ Ph.D. thesis, Colorado School of Mines, 2017.

Technical reports

• Q. Li, Z. Zhu, and G. Tang, ‘‘Geometry of Factored Nuclear Norm Regularization,’’ preprint, 2017.