Taichi Nakamura

140 total papers · 1.4k total citations
70 papers, 961 citations indexed

About

Taichi Nakamura is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Taichi Nakamura has authored 70 papers receiving a total of 961 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 9 papers in Information Systems. Recurrent topics in Taichi Nakamura's work include Model Reduction and Neural Networks (6 papers), Fluid Dynamics and Turbulent Flows (6 papers) and Software Engineering Techniques and Practices (5 papers). Taichi Nakamura is often cited by papers focused on Model Reduction and Neural Networks (6 papers), Fluid Dynamics and Turbulent Flows (6 papers) and Software Engineering Techniques and Practices (5 papers). Taichi Nakamura collaborates with scholars based in Japan, United States and United Kingdom. Taichi Nakamura's co-authors include Koji Fukagata, Hiroyuki Kono, Kai Fukami, Kazuto Hasegawa, Hisaho Hashimoto, Yuuichi Shimizu, Kazuo Kondo, Hitoshi Sakano, Naoki Mukawa and Naoki Okamoto and has published in prestigious journals such as Journal of Biological Chemistry, Scientific Reports and Journal of Computational Physics.

In The Last Decade

Taichi Nakamura

63 papers receiving 937 citations

Hit Papers

Convolutional neural netw... 2021 2026 2022 2024 2021 40 80 120

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Taichi Nakamura 287 244 111 99 99 70 961
Adel Mhamdi 120 0.4× 58 0.2× 14 0.1× 75 0.8× 68 0.7× 79 1.1k
Duo Xu 58 0.2× 19 0.1× 48 0.4× 38 0.4× 64 0.6× 86 1.2k
Wenzheng Wang 80 0.3× 67 0.3× 123 1.1× 106 1.1× 33 0.3× 86 944
Li Chen 77 0.3× 52 0.2× 228 2.1× 30 0.3× 227 2.3× 84 1.1k
Yanqing Chen 197 0.7× 18 0.1× 105 0.9× 74 0.7× 7 0.1× 45 1.2k
Hooman Fatoorehchi 99 0.3× 202 0.8× 6 0.1× 17 0.2× 168 1.7× 57 1.2k
Yue Yu 35 0.1× 63 0.3× 92 0.8× 102 1.0× 15 0.2× 100 1.2k
Qunfei Zhang 59 0.2× 19 0.1× 28 0.3× 173 1.7× 78 0.8× 159 1.1k
Wang Hui 217 0.8× 107 0.4× 27 0.2× 188 1.9× 27 0.3× 73 944
Fanghui Liu 40 0.1× 28 0.1× 211 1.9× 55 0.6× 26 0.3× 71 1.1k

Countries citing papers authored by Taichi Nakamura

Since Specialization
Citations

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

Fields of papers citing papers by Taichi Nakamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taichi Nakamura

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

All Works

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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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