Mingsi Tong

647 total citations
43 papers, 497 citations indexed

About

Mingsi Tong is a scholar working on Computer Vision and Pattern Recognition, Mechanical Engineering and Biomedical Engineering. According to data from OpenAlex, Mingsi Tong has authored 43 papers receiving a total of 497 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 8 papers in Mechanical Engineering and 8 papers in Biomedical Engineering. Recurrent topics in Mingsi Tong's work include Zebrafish Biomedical Research Applications (7 papers), Optical measurement and interference techniques (5 papers) and Cell Image Analysis Techniques (4 papers). Mingsi Tong is often cited by papers focused on Zebrafish Biomedical Research Applications (7 papers), Optical measurement and interference techniques (5 papers) and Cell Image Analysis Techniques (4 papers). Mingsi Tong collaborates with scholars based in China, United States and Canada. Mingsi Tong's co-authors include Weiyang Lin, Wei Chu, Zhan Li, Huijun Gao, Xinghu Yu, Songlin Zhuang, Yunlu Pan, Xuezeng Zhao, Hao Zhang and Jianbin Qiu and has published in prestigious journals such as Science Advances, IEEE Access and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Mingsi Tong

38 papers receiving 488 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mingsi Tong China 14 136 130 72 65 53 43 497
Shogo Arai Japan 12 121 0.9× 118 0.9× 70 1.0× 23 0.4× 36 0.7× 40 369
Adrian Burlacu Romania 12 138 1.0× 249 1.9× 45 0.6× 53 0.8× 15 0.3× 71 534
Min Zheng China 11 56 0.4× 58 0.4× 95 1.3× 31 0.5× 21 0.4× 29 594
Liwei Zhang China 12 83 0.6× 226 1.7× 78 1.1× 40 0.6× 13 0.2× 68 466
Jeremy Maitin-Shepard United States 5 212 1.6× 138 1.1× 85 1.2× 38 0.6× 54 1.0× 6 509
Chen-Chiung Hsieh Taiwan 10 63 0.5× 230 1.8× 31 0.4× 84 1.3× 35 0.7× 50 462
Ieee Robotics 8 98 0.7× 145 1.1× 82 1.1× 67 1.0× 12 0.2× 43 357
Xu Ma China 10 109 0.8× 45 0.3× 49 0.7× 133 2.0× 12 0.2× 44 511
Takuya Akashi Japan 11 102 0.8× 151 1.2× 23 0.3× 26 0.4× 13 0.2× 92 442
Juan Carlos Martínez‐García Mexico 12 285 2.1× 37 0.3× 43 0.6× 43 0.7× 19 0.4× 64 656

Countries citing papers authored by Mingsi Tong

Since Specialization
Citations

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

Fields of papers citing papers by Mingsi Tong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingsi Tong

This figure shows the co-authorship network connecting the top 25 collaborators of Mingsi Tong. A scholar is included among the top collaborators of Mingsi Tong 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 Mingsi Tong. Mingsi Tong 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.
Lu, Zhihui, et al.. (2025). Robust Control of Heterogeneous Biohybrid Microrobots With Actuator Uncertainty Compensation. IEEE/ASME Transactions on Mechatronics. 1–9.
2.
Li, Ke, Yanfang Ren, Dongming Lin, et al.. (2025). Green preparation of bimetallic CuO/ZnO nanoparticles by using Cinnamomum camphora (L.) leaf and its potential for antifungal and cadmium removal applications. Inorganic Chemistry Communications. 178. 114577–114577. 5 indexed citations
3.
Ren, Yanfang, et al.. (2025). Application of nano chitosan synthesized from Exopalaemon modestus shell to control the infection of cherry tomato leaves by Alternaria alternata. International Journal of Biological Macromolecules. 308(Pt 2). 142456–142456. 1 indexed citations
4.
Ren, Yanfang, et al.. (2025). Biodegradable chitosan-based films decorated with biosynthetic copper oxide nanoparticle for post-harvest tomato preservation. International Journal of Biological Macromolecules. 295. 139595–139595. 18 indexed citations
5.
Tong, Mingsi, Songlin Zhuang, Xinghu Yu, et al.. (2025). Robotic micromanipulation for patterned and complex organoid biofabrication. Science Advances. 11(36). eadz0808–eadz0808.
6.
Yu, Xinghu, et al.. (2025). A Robotic Micromanipulation System for Homogeneous Organoid Culture. IEEE Transactions on Automation Science and Engineering. 22. 13061–13072. 2 indexed citations
7.
Tong, Mingsi, et al.. (2025). Preparation of chitosan-based coating enriched with zinc oxide nanoparticles for inhibiting Alternaria alternata on tomato fruits. Journal of Food Measurement & Characterization. 19(7). 4892–4907.
8.
Zhuang, Songlin, Xinghu Yu, Mingsi Tong, et al.. (2023). Microinjection in Biomedical Applications: An Effortless Autonomous Omnidirectional Microinjection System. IEEE Industrial Electronics Magazine. 18(4). 55–68. 53 indexed citations
9.
Yu, Xinghu, et al.. (2021). An improved automated zebrafish larva high-throughput imaging system. Computers in Biology and Medicine. 136. 104702–104702. 9 indexed citations
10.
Wang, Chunxiang, et al.. (2021). DanioSense: Automated High-Throughput Quantification of Zebrafish Larvae Group Movement. IEEE Transactions on Automation Science and Engineering. 19(2). 1058–1069. 11 indexed citations
11.
Tong, Mingsi, Xinghu Yu, Junjie Shao, et al.. (2020). Automated measuring method based on Machine learning for optomotor response in mice. Neurocomputing. 418. 241–250. 2 indexed citations
12.
Tong, Mingsi, et al.. (2020). A model-free fuzzy adaptive trajectory tracking control algorithm based on dynamic surface control. International Journal of Advanced Robotic Systems. 17(1). 41 indexed citations
13.
Tong, Mingsi, et al.. (2020). Weakly-Supervised Semantic Segmentation With Regional Location Cutting and Dynamic Credible Regions Correction. IEEE Access. 8. 204378–204388. 2 indexed citations
14.
Zhang, Hao, et al.. (2016). Correlation of firing pin impressions based on congruent matching cross-sections (CMX) method. Forensic Science International. 263. 186–193. 12 indexed citations
15.
Tong, Mingsi, et al.. (2015). An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications. Journal of Research of the National Institute of Standards and Technology. 120. 102–102. 12 indexed citations
16.
Zhang, Hao, et al.. (2015). A Simple and Fast Spline Filtering Algorithm for Surface Metrology. Journal of Research of the National Institute of Standards and Technology. 120. 129–129. 3 indexed citations
17.
Zhang, Hao, Mingsi Tong, & Wei Chu. (2015). An Areal Isotropic Spline Filter for Surface Metrology. Journal of Research of the National Institute of Standards and Technology. 120. 64–64. 13 indexed citations
18.
Yen, James H., et al.. (2014). The Second National Ballistics Imaging Comparison (NBIC-2). Journal of Research of the National Institute of Standards and Technology. 119. 644–644. 2 indexed citations
19.
Tong, Mingsi, et al.. (2014). Fired Cartridge Case Identification Using Optical Images and the Congruent Matching Cells (CMC) Method. Journal of Research of the National Institute of Standards and Technology. 119. 575–575. 24 indexed citations
20.
Han, Wen Bo, et al.. (2007). Superplastic Forming and Diffusion Bonding for Four-Layer Sheets Structure of Nickel-Base Superalloy. Materials science forum. 551-552. 163–168. 1 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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