Ming Yan

2.5k total citations
86 papers, 1.3k citations indexed

About

Ming Yan is a scholar working on Computational Mechanics, Biomedical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Ming Yan has authored 86 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computational Mechanics, 25 papers in Biomedical Engineering and 19 papers in Electrical and Electronic Engineering. Recurrent topics in Ming Yan's work include Sparse and Compressive Sensing Techniques (29 papers), Medical Imaging Techniques and Applications (14 papers) and Advanced X-ray and CT Imaging (12 papers). Ming Yan is often cited by papers focused on Sparse and Compressive Sensing Techniques (29 papers), Medical Imaging Techniques and Applications (14 papers) and Advanced X-ray and CT Imaging (12 papers). Ming Yan collaborates with scholars based in United States, China and Singapore. Ming Yan's co-authors include Yifei Lou, Stanley Osher, Yi Yang, Zhi Li, Wei Shi, Wotao Yin, Tieyong Zeng, Zhimin Peng, Jun Liu and Luminita A. Vese and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Computational Physics and IEEE Transactions on Signal Processing.

In The Last Decade

Ming Yan

79 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming Yan United States 19 571 301 255 232 220 86 1.3k
Rachel Ward United States 19 811 1.4× 339 1.1× 266 1.0× 113 0.5× 366 1.7× 64 1.4k
Jinshan Zeng China 18 608 1.1× 574 1.9× 142 0.6× 221 1.0× 372 1.7× 72 1.7k
Mark Rudelson United States 20 1.0k 1.8× 290 1.0× 386 1.5× 169 0.7× 455 2.1× 46 2.3k
H. Hindi United States 14 691 1.2× 350 1.2× 140 0.5× 241 1.0× 205 0.9× 49 2.0k
Brendan O’Donoghue United States 12 549 1.0× 262 0.9× 111 0.4× 159 0.7× 397 1.8× 20 1.6k
Raghunandan H. Keshavan United States 7 821 1.4× 393 1.3× 118 0.5× 128 0.6× 249 1.1× 11 1.3k
Parikshit Shah United States 12 647 1.1× 139 0.5× 262 1.0× 130 0.6× 135 0.6× 37 1.4k
Michael Strauss United States 13 770 1.3× 248 0.8× 246 1.0× 275 1.2× 228 1.0× 27 1.2k
C. Si̇nan Güntürk United States 12 594 1.0× 462 1.5× 327 1.3× 60 0.3× 126 0.6× 25 1.2k
Sangwoon Yun South Korea 20 1.1k 2.0× 819 2.7× 244 1.0× 253 1.1× 478 2.2× 61 2.3k

Countries citing papers authored by Ming Yan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Yan. A scholar is included among the top collaborators of Ming Yan 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 Ming Yan. Ming Yan 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.
Yan, Ming, et al.. (2026). Complex physics-informed neural network. Journal of Computational Physics. 553. 114713–114713. 1 indexed citations
2.
Yan, Ming, et al.. (2026). Convolution-weighting method for the physics-informed neural network: A primal-dual optimization perspective. Journal of Computational Physics. 555. 114773–114773.
3.
Yan, Ming, et al.. (2024). On the Improved Conditions for Some Primal-Dual Algorithms. Journal of Scientific Computing. 99(3). 2 indexed citations
4.
Yan, Ming, et al.. (2024). Sorted $$L_1/L_2$$ Minimization for Sparse Signal Recovery. Journal of Scientific Computing. 99(2).
5.
Hu, Mengqi, Yifei Lou, Bao Wang, et al.. (2023). Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum. Journal of Scientific Computing. 95(1). 2 indexed citations
6.
Yan, Ming, et al.. (2023). mixDA: mixup domain adaptation for glaucoma detection on fundus images. Neural Computing and Applications. 37(11). 7541–7560. 6 indexed citations
7.
Pu, Shi, et al.. (2022). Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks. IEEE Transactions on Signal Processing. 70. 1775–1787. 13 indexed citations
8.
Liu, Xiaorui, Yao Li, Rongrong Wang, Jiliang Tang, & Ming Yan. (2021). Linear Convergent Decentralized Optimization with Compression. International Conference on Learning Representations. 3 indexed citations
9.
Yan, Ming, et al.. (2020). Accelerated Schemes for the $L_1/L_2$ Minimization. IEEE Transactions on Signal Processing. 68. 2660–2669. 43 indexed citations
10.
Liu, Jun, Ming Yan, & Tieyong Zeng. (2019). Surface-Aware Blind Image Deblurring. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(3). 1041–1055. 64 indexed citations
11.
Hao, Yuning, Ming Yan, Blake R. Heath, Yu L. Lei, & Yuying Xie. (2019). Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares. PLoS Computational Biology. 15(5). e1006976–e1006976. 59 indexed citations
12.
Tang, Hanlin, Xiangru Lian, Ming Yan, Ce Zhang, & Ji Liu. (2018). $D^2$: Decentralized Training over Decentralized Data. International Conference on Machine Learning. 4848–4856. 1 indexed citations
13.
Li, Zhi & Ming Yan. (2017). A primal-dual algorithm with optimal stepsizes and its application in decentralized consensus optimization. arXiv (Cornell University). 5 indexed citations
14.
Yan, Ming. (2016). A Primal-dual Three-operator Splitting Scheme. arXiv (Cornell University). 2 indexed citations
15.
Yan, Ming. (2014). Certificateless Aggregate Signature Scheme. Dianzi Ke-ji Daxue xuebao. 4 indexed citations
16.
Zhang, Cishen, et al.. (2009). A Feldkamp-type approximate algorithm for helical multislice CT using extended scanning helix. Computerized Medical Imaging and Graphics. 33(3). 197–204. 2 indexed citations
17.
Yan, Ming, et al.. (2006). An Approximate Cone Beam Reconstruction Algorithm for Gantry‐Tilted CT Using Tangential Filtering. International Journal of Biomedical Imaging. 2006(1). 29370–29370. 1 indexed citations
18.
Ticknor, Anthony J., et al.. (2005). Efficient Passive and Active Wavelength-Stabilization Techniques for AWGs and Integrated Optical Filters. Optical Fiber Communication Conference. 7 indexed citations
19.
Liu, Alice Y.‐C., et al.. (2005). Low-Loss PLC Integration of Wavelength Blockers and Monitored VOA-Multiplexers for ROADM Applications. Optical Fiber Communication Conference. 1 indexed citations
20.
Yan, Ming & Cishen Zhang. (2005). Tilted plane Feldkamp type reconstruction algorithm for spiral cone beam CT. Medical Physics. 32(11). 3455–3467. 8 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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