Akira Imakura

979 total citations
71 papers, 538 citations indexed

About

Akira Imakura is a scholar working on Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics and Artificial Intelligence. According to data from OpenAlex, Akira Imakura has authored 71 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computational Theory and Mathematics, 27 papers in Atomic and Molecular Physics, and Optics and 22 papers in Artificial Intelligence. Recurrent topics in Akira Imakura's work include Matrix Theory and Algorithms (30 papers), Electromagnetic Scattering and Analysis (24 papers) and Advanced Optimization Algorithms Research (11 papers). Akira Imakura is often cited by papers focused on Matrix Theory and Algorithms (30 papers), Electromagnetic Scattering and Analysis (24 papers) and Advanced Optimization Algorithms Research (11 papers). Akira Imakura collaborates with scholars based in Japan, United States and China. Akira Imakura's co-authors include Tetsuya Sakurai, Xiucai Ye, Hongmin Li, Lei Du, Y Kaneda, Akira Fujii, Tetsuro Ohmori, Hideo Matsufuru, Kohsuke Sumiyoshi and Hiroki Nagakura and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Akira Imakura

62 papers receiving 523 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Akira Imakura Japan	13	154	104	98	74	73	71	538
Shing‐Tung Yau United States	11	38 0.2×	78 0.8×	52 0.5×	200 2.7×	3 0.0×	36	679
Kuo-Tsai Chen United States	15	43 0.3×	151 1.5×	195 2.0×	51 0.7×	23 0.3×	51	1.3k
Eduardo Martı́nez Spain	19	34 0.2×	40 0.4×	70 0.7×	249 3.4×	30 0.4×	61	906
Steven Rosenberg United States	13	75 0.5×	85 0.8×	53 0.5×	71 1.0×	23 0.3×	46	798
Federico Carminati Switzerland	9	107 0.7×	15 0.1×	156 1.6×	18 0.2×	42 0.6×	69	409
John Fitch United Kingdom	9	74 0.5×	76 0.7×	31 0.3×	23 0.3×	18 0.2×	50	326
Elisabeth M. Werner United States	17	220 1.4×	58 0.6×	4 0.0×	27 0.4×	182 2.5×	63	1.3k
Vasile Manta Romania	12	259 1.7×	94 0.9×	38 0.4×	42 0.6×	61 0.8×	64	543
Jean Martinet France	13	39 0.3×	53 0.5×	15 0.2×	43 0.6×	12 0.2×	49	712
L. S. Blackford United States	6	71 0.5×	130 1.3×	8 0.1×	8 0.1×	57 0.8×	10	543

Countries citing papers authored by Akira Imakura

Since Specialization

Citations

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

Fields of papers citing papers by Akira Imakura

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akira Imakura

This figure shows the co-authorship network connecting the top 25 collaborators of Akira Imakura. A scholar is included among the top collaborators of Akira Imakura 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 Akira Imakura. Akira Imakura 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

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

Takano, Yuichi, et al.. (2025). New solutions based on the generalized eigenvalue problem for the data collaboration analysis. Information Sciences. 723. 122642–122642.

Nakayama, Takeo, et al.. (2025). Data collaboration for causal inference from limited medical testing and medication data. Scientific Reports. 15(1). 9827–9827. 1 indexed citations

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

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

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

Li, Hongmin, Xiucai Ye, Akira Imakura, & Tetsuya Sakurai. (2023). LSEC: Large-scale spectral ensemble clustering. Intelligent Data Analysis. 27(1). 59–77. 5 indexed citations

Imakura, Akira, et al.. (2022). Application of data collaboration analysis to distributed data with misaligned features. Informatics in Medicine Unlocked. 32. 101013–101013. 3 indexed citations

Imakura, Akira, et al.. (2022). DC-COX: Data collaboration Cox proportional hazards model for privacy-preserving survival analysis on multiple parties. Journal of Biomedical Informatics. 137. 104264–104264. 12 indexed citations

10.

Huang, Meng, Xiucai Ye, Akira Imakura, & Tetsuya Sakurai. (2022). Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma. Journal of Biomedical Informatics. 128. 104049–104049. 4 indexed citations

11.

Gu, Xian‐Ming, et al.. (2020). Efficient variants of the CMRH method for solving a sequence of multi-shifted non-Hermitian linear systems simultaneously. Journal of Computational and Applied Mathematics. 375. 112788–112788. 4 indexed citations

12.

Imakura, Akira, et al.. (2019). Fast ADMM ℓ₁minimization by applying SMW formula and multi-row simultaneous estimation for Light Transport Matrix acquisition. 14–21.

13.

Imakura, Akira, et al.. (2019). Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation. 7–13.

14.

Imakura, Akira, et al.. (2018). An Improvement of the Nonlinear Semi-NMF Based Method by Considering Bias Vectors and Regularization for Deep Neural Networks. International Journal of Machine Learning and Computing. 8(3). 191–197. 1 indexed citations

15.

Nagakura, Hiroki, Wakana Iwakami, Shun Furusawa, et al.. (2017). Three-dimensional Boltzmann-Hydro Code for Core-collapse in Massive Stars. II. The Implementation of Moving-mesh for Neutron Star Kicks. The Astrophysical Journal Supplement Series. 229(2). 42–42. 38 indexed citations

16.

Gu, Xian‐Ming, et al.. (2016). Efficient variants of the CMRH method for solving multi-shifted non-Hermitian linear systems. arXiv (Cornell University). 2 indexed citations

17.

Gu, Xian‐Ming, et al.. (2016). Variants of the CMRH method for solving multi-shifted non-Hermitian linear systems. arXiv (Cornell University). 2 indexed citations

18.

Futamura, Yasunori, et al.. (2015). Filter analysis for the stochastic estimation of eigenvalue counts. JSIAM Letters. 7(0). 53–56.

19.

Imakura, Akira, Lei Du, & Tetsuya Sakurai. (2014). A block Arnoldi-type contour integral spectral projection method for solving generalized eigenvalue problems. Applied Mathematics Letters. 32. 22–27. 18 indexed citations

20.

Kaneda, Y, Akira Imakura, Akira Fujii, & Tetsuro Ohmori. (2002). Schizophrenia Quality of Life Scale: validation of the Japanese version. Psychiatry Research. 113(1-2). 107–113. 59 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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