Qing Ling

8.4k total citations · 3 hit papers
203 papers, 5.0k citations indexed

About

Qing Ling is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Qing Ling has authored 203 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Computer Networks and Communications, 74 papers in Artificial Intelligence and 58 papers in Computational Mechanics. Recurrent topics in Qing Ling's work include Distributed Control Multi-Agent Systems (59 papers), Sparse and Compressive Sensing Techniques (57 papers) and Stochastic Gradient Optimization Techniques (40 papers). Qing Ling is often cited by papers focused on Distributed Control Multi-Agent Systems (59 papers), Sparse and Compressive Sensing Techniques (57 papers) and Stochastic Gradient Optimization Techniques (40 papers). Qing Ling collaborates with scholars based in China, United States and Hong Kong. Qing Ling's co-authors include Wotao Yin, Wei Shi, Gang Wu, Kun Yuan, Zhi Tian, Alejandro Ribeiro, Tianyi Chen, Georgios B. Giannakis, Aryan Mokhtari and Wei Xu and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and IEEE Transactions on Automatic Control.

In The Last Decade

Qing Ling

188 papers receiving 4.8k citations

Hit Papers

EXTRA: An Exact First-Order Algorithm for Decentralized C... 2014 2026 2018 2022 2015 2014 2016 200 400 600

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qing Ling China 30 2.8k 2.0k 1.3k 968 448 203 5.0k
Michael Rabbat Canada 36 2.9k 1.0× 1.6k 0.8× 1.1k 0.9× 1.1k 1.2× 360 0.8× 121 5.3k
Mark Coates Canada 40 2.4k 0.9× 2.0k 1.0× 288 0.2× 990 1.0× 351 0.8× 185 4.8k
Tsung‐Hui Chang China 36 2.4k 0.9× 1.2k 0.6× 453 0.4× 3.7k 3.8× 459 1.0× 225 6.1k
Li Zhang China 36 2.7k 0.9× 606 0.3× 239 0.2× 2.5k 2.5× 493 1.1× 348 5.0k
Soummya Kar United States 42 4.1k 1.5× 1.9k 0.9× 599 0.5× 2.9k 2.9× 102 0.2× 230 7.2k
Ganapati Panda India 32 435 0.2× 1.7k 0.8× 730 0.6× 974 1.0× 598 1.3× 230 4.8k
Usman A. Khan United States 26 2.0k 0.7× 1.0k 0.5× 369 0.3× 887 0.9× 52 0.1× 126 2.9k
Denız Gündüz United Kingdom 50 5.5k 2.0× 4.2k 2.1× 422 0.3× 6.3k 6.5× 1.3k 2.9× 381 11.3k
Qilian Liang United States 30 1.4k 0.5× 626 0.3× 302 0.2× 1.7k 1.7× 236 0.5× 236 3.3k
J. Zico Kolter United States 32 913 0.3× 2.4k 1.2× 180 0.1× 1.2k 1.2× 954 2.1× 108 5.4k

Countries citing papers authored by Qing Ling

Since Specialization
Citations

This map shows the geographic impact of Qing Ling'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 Qing Ling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qing Ling more than expected).

Fields of papers citing papers by Qing Ling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qing Ling. 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 Qing Ling. The network helps show where Qing Ling may publish in the future.

Co-authorship network of co-authors of Qing Ling

This figure shows the co-authorship network connecting the top 25 collaborators of Qing Ling. A scholar is included among the top collaborators of Qing Ling 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 Qing Ling. Qing Ling is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ling, Qing, et al.. (2025). Generalization Error Matters in Decentralized Learning Under Byzantine Attacks. IEEE Transactions on Signal Processing. 73. 843–857. 3 indexed citations
2.
Ling, Qing, Yufeng Zhang, Wei Chang, et al.. (2024). NBR1-dependent autophagy activation protects against environmental cadmium-evoked placental trophoblast senescence. Chemosphere. 358. 142138–142138. 3 indexed citations
3.
Zhu, Hua-Long, Wei Chang, Yufeng Zhang, et al.. (2024). Maternal prednisone exposure during pregnancy elevates susceptibility to osteoporosis in female offspring: The role of mitophagy/FNDC5 alteration in skeletal muscle. Journal of Hazardous Materials. 469. 133997–133997. 6 indexed citations
4.
Ling, Qing, et al.. (2024). Dual-Domain Defenses for Byzantine-Resilient Decentralized Resource Allocation. IEEE Transactions on Signal and Information Processing over Networks. 10. 804–819. 1 indexed citations
5.
Zhu, Heng, et al.. (2023). C-RSA: Byzantine-robust and communication-efficient distributed learning in the non-convex and non-IID regime. Signal Processing. 213. 109222–109222. 2 indexed citations
6.
Zhu, Heng & Qing Ling. (2023). Byzantine-Robust Distributed Learning With Compression. IEEE Transactions on Signal and Information Processing over Networks. 9. 280–294. 8 indexed citations
7.
Wang, Bin, Jun Fang, Hongbin Li, Xiaojun Yuan, & Qing Ling. (2023). Confederated Learning: Federated Learning With Decentralized Edge Servers. IEEE Transactions on Signal Processing. 71. 248–263. 18 indexed citations
8.
Ling, Qing, et al.. (2023). Quantization Bits Allocation for Wireless Federated Learning. IEEE Transactions on Wireless Communications. 22(11). 8336–8351. 7 indexed citations
10.
Tan, Kay Chen, Limin Zhang, Wenjun Yan, et al.. (2022). A Semi-supervised Emitter Identification Method for Imbalanced Category. SHILAP Revista de lepidopterología.
11.
Ling, Qing, et al.. (2022). Byzantine-Resilient Resource Allocation Over Decentralized Networks. IEEE Transactions on Signal Processing. 70. 4711–4726. 15 indexed citations
12.
Chen, Shunli, et al.. (2021). Photocatalytic redox on the surface of colloidal silver nanoparticles revealed by second harmonic generation and two-photon luminescence. Physical Chemistry Chemical Physics. 23(35). 19752–19759. 5 indexed citations
13.
Zhang, Limin, et al.. (2021). Real-Time OFDM Signal Modulation Classification Based on Deep Learning and Software-Defined Radio. IEEE Communications Letters. 25(9). 2988–2992. 30 indexed citations
14.
Ling, Qing, et al.. (2019). Byzantine-resilient Distributed Large-scale Matrix Completion. 8167–8171. 8 indexed citations
15.
Meng, Qingpeng, et al.. (2018). Precision Analysis of the Large Scale Mapping Based on Sirius UAV. Bulletin of Surveying and Mapping. 158. 1 indexed citations
16.
Chen, Tianyi, Qing Ling, Yanning Shen, & Georgios B. Giannakis. (2018). Heterogeneous Online Learning for “Thing-Adaptive” Fog Computing in IoT. IEEE Internet of Things Journal. 5(6). 4328–4341. 26 indexed citations
17.
Ling, Qing, et al.. (2017). Evacuate Before Too Late: Distributed Backup in Inter-DC Networks with Progressive Disasters. IEEE Transactions on Parallel and Distributed Systems. 29(5). 1058–1074. 32 indexed citations
18.
Yuan, Xiaoyun, et al.. (2016). Magic Glasses: From 2D to 3D. IEEE Transactions on Circuits and Systems for Video Technology. 27(4). 843–854. 5 indexed citations
19.
Ling, Qing, Yangyang Xu, Wotao Yin, & Zaiwen Wen. (2012). Decentralized low-rank matrix completion. 2925–2928. 37 indexed citations
20.
Ling, Qing & Zhi Tian. (2009). A decentralized Gauss-Seidel approach for in-network sparse signal recovery. International Conference on Information Fusion. 380–387. 2 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.

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