This map shows the geographic impact of Zhihui Zhu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zhihui Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhihui Zhu more than expected).
This network shows the impact of papers produced by Zhihui Zhu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zhihui Zhu. The network helps show where Zhihui Zhu may publish in the future.
Co-authorship network of co-authors of Zhihui Zhu
This figure shows the co-authorship network connecting the top 25 collaborators of Zhihui Zhu.
A scholar is included among the top collaborators of Zhihui Zhu based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Zhihui Zhu. Zhihui Zhu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ding, Tianyu, Zhihui Zhu, Manolis C. Tsakiris, Renè Vidal, & Daniel P. Robinson. (2021). Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms. International Conference on Artificial Intelligence and Statistics. 2944–2952.3 indexed citations
7.
Qu, Qing, et al.. (2020). Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning. International Conference on Learning Representations.6 indexed citations
8.
Zhu, Zhihui, et al.. (2019). Distributed Low-rank Matrix Factorization With Exact Consensus. Neural Information Processing Systems. 32. 8420–8430.9 indexed citations
9.
Zhu, Zhihui, et al.. (2019). Noisy Dual Principal Component Pursuit. International Conference on Machine Learning. 1617–1625.7 indexed citations
10.
Zhu, Zhihui, et al.. (2019). A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning. Neural Information Processing Systems. 32. 9437–9447.6 indexed citations
11.
Li, Qiuwei, Zhihui Zhu, & Gongguo Tang. (2019). Alternating Minimizations Converge to Second-Order Optimal Solutions. International Conference on Machine Learning. 3935–3943.23 indexed citations
12.
Li, Xiao, et al.. (2019). Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods.. arXiv (Cornell University).3 indexed citations
Zhu, Zhihui, Yifan Wang, Daniel P. Robinson, et al.. (2018). Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms. Neural Information Processing Systems. 31. 2171–2181.10 indexed citations
Zhu, Zhihui, Qiuwei Li, Gongguo Tang, & Michael B. Wakin. (2017). The Global Optimization Geometry of Nonsymmetric Matrix Factorization and Sensing.. arXiv (Cornell University).8 indexed citations
17.
Zhu, Zhihui & Michael B. Wakin. (2015). Detection of stationary targets using Discrete Prolate Spheroidal Sequences. 1–2.1 indexed citations
18.
Li, Qiuwei, et al.. (2013). Projection matrix optimization based on SVD for compressive sensing systems. Chinese Control Conference. 4820–4825.3 indexed citations
19.
Bai, Huang, et al.. (2013). Design of Optimal Measurement Matrix for Compressive Detection. 1–5.4 indexed citations
20.
Zhu, Zhihui, et al.. (2007). Application of EMD and SVD in fault identification. International Power Engineering Conference. 1247–1250.3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.