Tetsuya Sakurai

3.3k total citations
157 papers, 1.9k citations indexed

About

Tetsuya Sakurai is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Numerical Analysis. According to data from OpenAlex, Tetsuya Sakurai has authored 157 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Computational Theory and Mathematics, 37 papers in Artificial Intelligence and 34 papers in Numerical Analysis. Recurrent topics in Tetsuya Sakurai's work include Matrix Theory and Algorithms (52 papers), Electromagnetic Scattering and Analysis (25 papers) and Face and Expression Recognition (19 papers). Tetsuya Sakurai is often cited by papers focused on Matrix Theory and Algorithms (52 papers), Electromagnetic Scattering and Analysis (25 papers) and Face and Expression Recognition (19 papers). Tetsuya Sakurai collaborates with scholars based in Japan, China and Belgium. Tetsuya Sakurai's co-authors include Xiucai Ye, Hiroshi Sugiura, Hiroto Tadano, Tsutomu Ikegami, Akira Imakura, Yasunori Futamura, Leyi Wei, Lesong Wei, Umpei Nagashima and Kinji Kimura and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Tetsuya Sakurai

142 papers receiving 1.9k citations

Peers

Tetsuya Sakurai

CTM

AMPO

Alfio Borzı̀ Germany

Arno B. J. Kuijlaars Belgium

Benedict Leimkuhler United Kingdom

Edward B. Saff United States

Efstratios Gallopoulos Greece

Javier Segura Spain

Robert J. Plemmons United States

Shmuel Friedland United States

Guo-Wei Wei United States

Ilse C. F. Ipsen United States

Alfio Borzı̀ Germany

Tetsuya Sakurai

416 ×0.5

CTM

308 ×0.6

AMPO

358 ×0.9

341 ×0.8

240 ×0.9

Citations per year, relative to Tetsuya Sakurai Tetsuya Sakurai (= 1×) peers Alfio Borzı̀

Countries citing papers authored by Tetsuya Sakurai

Since Specialization

Citations

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

Fields of papers citing papers by Tetsuya Sakurai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tetsuya Sakurai

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

All Works

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20 of 20 papers shown

Wang, Li, Xiangzheng Fu, Xiucai Ye, et al.. (2025). MOFormer: navigating the antimicrobial peptide design space with Pareto-based multi-objective transformer. Briefings in Bioinformatics. 26(6).

Nakayama, Takeo, et al.. (2025). Anomaly detection in double-entry bookkeeping data by federated learning system with non-model sharing approach. Scientific Reports. 15(1). 42208–42208.

Imakura, Akira, et al.. (2025). Estimation of conditional average treatment effects on distributed confidential data. Expert Systems with Applications. 296. 129066–129066. 1 indexed citations

Huang, Dong, Xiucai Ye, & Tetsuya Sakurai. (2024). Multi-party collaborative drug discovery via federated learning. Computers in Biology and Medicine. 171. 108181–108181. 5 indexed citations

Ye, Xiucai, et al.. (2024). Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping. Methods. 233. 52–60. 1 indexed citations

Liu, Tianyuan, Zixu Wang, Xinyan Yang, et al.. (2024). CodLncScape Provides a Self‐Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs. Advanced Science. 11(22). e2400009–e2400009. 26 indexed citations

Lu, Anh Khoa Augustin, Jianbo Lin, Yasunori Futamura, et al.. (2024). Extraction of local structure differences in silica based on unsupervised learning. Physical Chemistry Chemical Physics. 26(15). 11657–11666.

Ye, Xiucai, et al.. (2023). Interactive gene identification for cancer subtyping based on multi-omics clustering. Methods. 211. 61–67. 6 indexed citations

Nguyen, Hai & Tetsuya Sakurai. (2023). Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains. Machine Learning. 112(8). 2845–2869.

10.

Lin, Jianbo, Ryo Tamura, Yasunori Futamura, Tetsuya Sakurai, & Tsuyoshi Miyazaki. (2023). Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces. Physical Chemistry Chemical Physics. 25(27). 17978–17986. 4 indexed citations

11.

Imakura, Akira, et al.. (2023). Collaborative causal inference on distributed data. Expert Systems with Applications. 244. 123024–123024. 3 indexed citations

12.

Imakura, Akira & Tetsuya Sakurai. (2023). Complex moment-based eigensolver coupled with two Krylov subspaces. Journal of Computational and Applied Mathematics. 432. 115283–115283.

13.

Imakura, Akira, et al.. (2023). DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning. SHILAP Revista de lepidopterología. 3(3). 197–210. 10 indexed citations

14.

Wei, Lesong, Xiucai Ye, Kai Zhang, et al.. (2022). SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. Briefings in Bioinformatics. 24(1). 37 indexed citations

15.

Wei, Lesong, Xiucai Ye, Tetsuya Sakurai, Zengchao Mu, & Leyi Wei. (2022). ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning. Bioinformatics. 38(6). 1514–1524. 111 indexed citations

16.

Tamura, Ryo, et al.. (2022). Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations. Physical review. B.. 105(7). 7 indexed citations

17.

Ye, Xiucai, et al.. (2016). Spectral clustering and discriminant analysis for unsupervised feature selection.. The European Symposium on Artificial Neural Networks. 1 indexed citations

18.

Shimizu, Noritaka, Y. Utsuno, Yasunori Futamura, et al.. (2015). Stochastic estimation of nuclear level density in the nuclear shell model: An application to parity-dependent level density in 58 Ni. Physics Letters B. 753. 13–17. 18 indexed citations

19.

Sakurai, Tetsuya, Hiroto Tadano, Tsutomu Ikegami, & Umpei Nagashima. (2010). A parallel eigensolver using contour integration for generalized eigenvalue problems in molecular simulation. Taiwanese Journal of Mathematics. 14. 855–867. 3 indexed citations

20.

Sakurai, Tetsuya, et al.. (1996). A method for locating clusters of zeros of analytic functions. ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 76. 515–516. 2 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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