Chunmao Wang

953 total citations
20 papers, 672 citations indexed

About

Chunmao Wang is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Signal Processing. According to data from OpenAlex, Chunmao Wang has authored 20 papers receiving a total of 672 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Cognitive Neuroscience and 4 papers in Signal Processing. Recurrent topics in Chunmao Wang's work include Face recognition and analysis (6 papers), Neural dynamics and brain function (4 papers) and Emotion and Mood Recognition (3 papers). Chunmao Wang is often cited by papers focused on Face recognition and analysis (6 papers), Neural dynamics and brain function (4 papers) and Emotion and Mood Recognition (3 papers). Chunmao Wang collaborates with scholars based in China, United States and Hungary. Chunmao Wang's co-authors include Mark Vangel, Jian Kong, Randy L. Gollub, Elizabeth F. Chua, Kenneth K. Kwong, István Ulbert, Shiliang Pu, Jingjing Wang, Orrin Devinsky and Joseph R. Madsen and has published in prestigious journals such as Science, Biochemical and Biophysical Research Communications and Hippocampus.

In The Last Decade

Chunmao Wang

19 papers receiving 652 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chunmao Wang China	9	429	111	106	101	84	20	672
Arthur C. Tsai Taiwan	11	171 0.4×	10 0.1×	60 0.6×	44 0.4×	12 0.1×	22	331
Jun-Yun Zhang China	12	887 2.1×	15 0.1×	98 0.9×	113 1.1×	18 0.2×	21	978
Julie Epelboim United States	14	473 1.1×	24 0.2×	17 0.2×	116 1.1×	14 0.2×	20	666
Hilda M. Fehd United States	5	507 1.2×	26 0.2×	23 0.2×	58 0.6×	5 0.1×	8	612
Minnan Xu-Wilson United States	10	437 1.0×	10 0.1×	37 0.3×	26 0.3×	10 0.1×	15	667
Kosuke Itoh Japan	13	351 0.8×	13 0.1×	32 0.3×	134 1.3×	25 0.3×	53	545
Natalia I. Córdova United States	7	377 0.9×	7 0.1×	52 0.5×	45 0.4×	8 0.1×	7	538
Kimberly Meier Canada	11	342 0.8×	7 0.1×	30 0.3×	55 0.5×	40 0.5×	28	423
Matthias Nau Germany	10	435 1.0×	7 0.1×	87 0.8×	31 0.3×	4 0.0×	17	538
Anne Cheylus France	15	368 0.9×	64 0.6×	6 0.1×	201 2.0×	9 0.1×	27	597

Countries citing papers authored by Chunmao Wang

Since Specialization

Citations

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

Fields of papers citing papers by Chunmao Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunmao Wang

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Chunmao, et al.. (2025). TIM-3 teams up with PD-1 in cancer immunotherapy: mechanisms and perspectives. Molecular Biomedicine. 6(1). 27–27. 12 indexed citations

Wang, Jingjing, et al.. (2022). Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition. IEEE Transactions on Multimedia. 25. 1760–1772. 6 indexed citations

Bian, Ying, Peng Zhang, Jingjing Wang, Chunmao Wang, & Shiliang Pu. (2022). Learning Multiple Explainable and Generalizable Cues for Face Anti-Spoofing. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2310–2314. 14 indexed citations

Yan, Jingwei, Jingjing Wang, Qiang Li, Chunmao Wang, & Shiliang Pu. (2021). Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition. 1038–1046. 7 indexed citations

Zhang, Yanyi, Ming Kong, Tianqi Zhao, et al.. (2021). Auxiliary diagnostic system for ADHD in children based on AI technology. Frontiers of Information Technology & Electronic Engineering. 22(3). 400–414. 8 indexed citations

Wang, Jingjing, et al.. (2021). Deep Spiking Neural Network for High-Accuracy and Energy-Efficient Face Action Unit Recognition. 10. 1–7. 1 indexed citations

Zhang, Xiaofeng, et al.. (2021). Demodalizing Face Recognition with Synthetic Samples. Proceedings of the AAAI Conference on Artificial Intelligence. 35(4). 3278–3286. 6 indexed citations

Yan, Jingwei, Boyuan Jiang, Jingjing Wang, et al.. (2021). Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition. 10. 2005–2009. 3 indexed citations

Wang, Jingjing, et al.. (2021). Self-Domain Adaptation for Face Anti-Spoofing. Proceedings of the AAAI Conference on Artificial Intelligence. 35(4). 2746–2754. 69 indexed citations

10.

Wang, Chunmao, et al.. (2017). A method of frame synchronization for high speed pulse signal based on GTX. 34. 1348–1352. 4 indexed citations

11.

Wang, Chunmao, et al.. (2012). Anterolateral minithoracotomy versus median sternotomy for the treatment of congenital heart defects: a meta-analysis and systematic review. Journal of Cardiothoracic Surgery. 7(1). 43–43. 13 indexed citations

12.

Kong, Minjian, et al.. (2012). Tissue distribution and cancer growth inhibition of magnetic lipoplex-delivered type 1 insulin-like growth factor receptor shRNA in nude mice. Acta Biochimica et Biophysica Sinica. 44(7). 591–596. 10 indexed citations

13.

Wang, Chunmao, Minjian Kong, & Aiqiang Dong. (2012). [Magnetic liposome mediated shRNA specifically suppresses the growth of non-small cell lung cancer in vitro and in vivo].. PubMed. 92(5). 341–4. 1 indexed citations

14.

Wang, Chunmao. (2011). New Application of Wireless Sensor Network in Bridge-health Monitoring. Computer and Modernization.

15.

Wang, Chunmao, et al.. (2011). Tumor-targeting magnetic lipoplex delivery of short hairpin RNA suppresses IGF-1R overexpression of lung adenocarcinoma A549 cells in vitro and in vivo. Biochemical and Biophysical Research Communications. 410(3). 537–542. 36 indexed citations

16.

Cash, Sydney S., Eric Halgren, Nima Dehghani, et al.. (2010). Response to Comment on “The Human K-Complex Represents an Isolated Cortical Down-State”. Science. 330(6000). 35–35. 3 indexed citations

17.

Cash, Sydney S., Eric Halgren, Nima Dehghani, et al.. (2009). The Human K-Complex Represents an Isolated Cortical Down-State. Science. 324(5930). 1084–1087. 272 indexed citations

18.

Steinvorth, Sarah, Chunmao Wang, István Ulbert, Donald L. Schomer, & Eric Halgren. (2009). Human entorhinal gamma and theta oscillations selective for remote autobiographical memory. Hippocampus. 20(1). 166–173. 41 indexed citations

19.

Wang, Chunmao. (2008). Applications of TOFD of Ultrasonic Imaging Detection Technology in Pressure Vessels Inspection. Pressure vessel Technology. 1 indexed citations

20.

Kong, Jian, Chunmao Wang, Kenneth K. Kwong, et al.. (2005). The neural substrate of arithmetic operations and procedure complexity. Cognitive Brain Research. 22(3). 397–405. 165 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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