Ming Yan

2.9k total citations · 1 hit paper
93 papers, 2.0k citations indexed

About

Ming Yan is a scholar working on Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials and Artificial Intelligence. According to data from OpenAlex, Ming Yan has authored 93 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Atomic and Molecular Physics, and Optics, 28 papers in Electronic, Optical and Magnetic Materials and 26 papers in Artificial Intelligence. Recurrent topics in Ming Yan's work include Magnetic properties of thin films (38 papers), Magnetic Properties and Applications (22 papers) and Topic Modeling (16 papers). Ming Yan is often cited by papers focused on Magnetic properties of thin films (38 papers), Magnetic Properties and Applications (22 papers) and Topic Modeling (16 papers). Ming Yan collaborates with scholars based in China, United States and Singapore. Ming Yan's co-authors include D. J. Sellmyer, Zhipeng Liu, Zhiyong Jiang, Nathan Powers, Hao Zeng, D. J. Sellmyer, R. P. H. Chang, Xiaoqing Wang, Xiaoqing Wang and Yuncong Chen and has published in prestigious journals such as Angewandte Chemie International Edition, Physical review. B, Condensed matter and Applied Physics Letters.

In The Last Decade

Ming Yan

88 papers receiving 2.0k citations

Hit Papers

A Borondifluoride‐Complex‐Based Photothermal Agent with a... 2021 2026 2022 2024 2021 50 100 150 200

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 China 23 823 679 667 467 214 93 2.0k
Semion K. Saikin United States 24 1.0k 1.2× 899 1.3× 375 0.6× 413 0.9× 614 2.9× 66 2.4k
Hyun Joon Shin South Korea 30 294 0.4× 489 0.7× 309 0.5× 314 0.7× 450 2.1× 103 2.8k
Ryo Tamura Japan 25 415 0.5× 1.3k 1.9× 221 0.3× 312 0.7× 453 2.1× 117 2.2k
David Gray United States 33 414 0.5× 1.2k 1.8× 913 1.4× 1.3k 2.8× 420 2.0× 111 4.3k
Takashi Fukuda Japan 29 697 0.8× 1.2k 1.8× 1.1k 1.7× 538 1.2× 1.6k 7.5× 268 4.2k
Jonathan Schmidt Germany 18 466 0.6× 2.0k 3.0× 233 0.3× 273 0.6× 699 3.3× 33 3.1k
Jiajun Ma China 22 718 0.9× 497 0.7× 313 0.5× 317 0.7× 390 1.8× 197 2.2k
Yuxia Zhao China 32 1.3k 1.6× 1.4k 2.0× 630 0.9× 1.2k 2.5× 796 3.7× 135 4.0k

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.
Wang, Yanli, et al.. (2025). A machine solution for math word problems based on semantic understanding enhancement. Scientific Reports. 15(1). 36565–36565.
2.
Liu, Hao, Yong Zhou, Bing Liu, Ming Yan, & Joey Tianyi Zhou. (2024). L2A: Learning Affinity From Attention for Weakly Supervised Continual Semantic Segmentation. IEEE Transactions on Circuits and Systems for Video Technology. 35(1). 315–328. 2 indexed citations
3.
Ma, Ying, et al.. (2024). Target-Embedding Autoencoder With Knowledge Distillation for Multi-Label Classification. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(3). 2506–2517. 4 indexed citations
4.
Ren, Lei, et al.. (2024). The passive optimization mechanism of winter thermal performance in commercial complex based on coupled multi-spatial parameters. Journal of Building Engineering. 96. 110579–110579. 1 indexed citations
5.
Liu, Haoran, Ying Ma, Ming Yan, et al.. (2024). DiDA: Disambiguated Domain Alignment for Cross-Domain Retrieval with Partial Labels. Proceedings of the AAAI Conference on Artificial Intelligence. 38(4). 3612–3620. 2 indexed citations
6.
Fan, Mei‐Yi, Wenqi Zhang, Yanlin Zhang, et al.. (2023). Formation Mechanisms and Source Apportionments of Nitrate Aerosols in a Megacity of Eastern China Based On Multiple Isotope Observations. Journal of Geophysical Research Atmospheres. 128(6). 12 indexed citations
8.
Ma, Ying, et al.. (2022). Attention-based Local Mean K-Nearest Centroid Neighbor Classifier. Expert Systems with Applications. 201. 117159–117159. 11 indexed citations
9.
10.
Li, Yue, et al.. (2021). Joint protection strategies for Saccharomyces boulardii: exogenous encapsulation and endogenous biofilm structure. Applied Microbiology and Biotechnology. 105(21-22). 8469–8479. 7 indexed citations
11.
Jiang, Zhiyong, Changli Zhang, Xiaoqing Wang, et al.. (2021). A Borondifluoride‐Complex‐Based Photothermal Agent with an 80 % Photothermal Conversion Efficiency for Photothermal Therapy in the NIR‐II Window. Angewandte Chemie. 133(41). 22550–22558. 24 indexed citations
12.
Jiang, Zhiyong, Changli Zhang, Xiaoqing Wang, et al.. (2021). A Borondifluoride‐Complex‐Based Photothermal Agent with an 80 % Photothermal Conversion Efficiency for Photothermal Therapy in the NIR‐II Window. Angewandte Chemie International Edition. 60(41). 22376–22384. 249 indexed citations breakdown →
13.
Yan, Ming, Jianxi Yang, Cen Chen, et al.. (2021). Enhanced gradient learning for deep neural networks. IET Image Processing. 16(2). 365–377. 1 indexed citations
14.
Li, Chenliang, Bin Bi, Ming Yan, Wei Wang, & Songfang Huang. (2021). Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction. 942–947. 9 indexed citations
15.
Yan, Ming, Cen Chen, Jiawei Du, et al.. (2021). Memory-Assistant Collaborative Language Understanding for Artificial Intelligence of Things. IEEE Transactions on Industrial Informatics. 18(5). 3349–3357. 3 indexed citations
16.
Yan, Ming & Yi Pan. (2021). Meta-learning for compressed language model: A multiple choice question answering study. Neurocomputing. 487. 181–189. 4 indexed citations
17.
Yan, Ming, Chenliang Li, Chen Wu, et al.. (2019). IDST at TREC 2019 Deep Learning Track: Deep Cascade Ranking with Generation-based Document Expansion and Pre-trained Language Modeling.. Text REtrieval Conference. 14 indexed citations
18.
Liu, Zhipeng, Zhiyong Jiang, Ming Yan, & Xiaoqing Wang. (2019). Recent Progress of BODIPY Dyes With Aggregation-Induced Emission. Frontiers in Chemistry. 7. 712–712. 159 indexed citations
19.
Chen, Xiaojun, et al.. (2014). Homogeneously ultrasensitive electrochemical detection of adenosine triphosphate based on multiple signal amplification strategy. Biosensors and Bioelectronics. 58. 48–56. 18 indexed citations
20.
Sabirianov, Renat, et al.. (2002). Curie temperature of FePt: B2O3 nanocomposite films. Physical Review B. 66(18). 1844251–1844256. 6 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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