Hiroyuki Sato

3.5k total citations
113 papers, 1.8k citations indexed

About

Hiroyuki Sato is a scholar working on Plant Science, Genetics and Numerical Analysis. According to data from OpenAlex, Hiroyuki Sato has authored 113 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Plant Science, 29 papers in Genetics and 13 papers in Numerical Analysis. Recurrent topics in Hiroyuki Sato's work include Genetic Mapping and Diversity in Plants and Animals (22 papers), Rice Cultivation and Yield Improvement (18 papers) and Plant Disease Resistance and Genetics (16 papers). Hiroyuki Sato is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (22 papers), Rice Cultivation and Yield Improvement (18 papers) and Plant Disease Resistance and Genetics (16 papers). Hiroyuki Sato collaborates with scholars based in Japan, United States and Cambodia. Hiroyuki Sato's co-authors include Tokio Imbe, Masahiro Yano, Hideyuki Hirabayashi, Ikuo Ando, Johannes Ø. Røyset, Hiroaki Okamoto, Shigeo Nagashima, Jirintai, Masaharu Takahashi and Ritsuko Mizobuchi and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and Scientific Reports.

In The Last Decade

Hiroyuki Sato

106 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hiroyuki Sato Japan 24 858 424 280 248 173 113 1.8k
Osamu Kaneko Japan 39 112 0.1× 110 0.3× 36 0.1× 197 0.8× 707 4.1× 242 4.8k
Jack Williams United Kingdom 20 125 0.1× 49 0.1× 28 0.1× 116 0.5× 443 2.6× 44 1.9k
David W. Gohara United States 20 203 0.2× 198 0.5× 37 0.1× 244 1.0× 915 5.3× 30 2.3k
H. A. Wood United States 32 389 0.5× 468 1.1× 11 0.0× 155 0.6× 2.4k 14.1× 73 3.3k
Pushpendra Kumar India 28 172 0.2× 118 0.3× 15 0.1× 147 0.6× 71 0.4× 123 2.5k
Rashid Jan Saudi Arabia 34 131 0.2× 147 0.3× 15 0.1× 196 0.8× 94 0.5× 190 2.6k
Parvaiz Ahmad Naik China 27 64 0.1× 284 0.7× 13 0.0× 130 0.5× 220 1.3× 66 2.1k
Amin Jajarmi Iran 45 108 0.1× 186 0.4× 19 0.1× 164 0.7× 87 0.5× 82 5.6k
Sania Qureshi Pakistan 33 60 0.1× 171 0.4× 11 0.0× 222 0.9× 44 0.3× 111 3.7k
Filippo Castiglione Italy 28 136 0.2× 142 0.3× 54 0.2× 610 2.5× 2.1k 12.2× 130 3.5k

Countries citing papers authored by Hiroyuki Sato

Since Specialization
Citations

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

Fields of papers citing papers by Hiroyuki Sato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hiroyuki Sato

This figure shows the co-authorship network connecting the top 25 collaborators of Hiroyuki Sato. A scholar is included among the top collaborators of Hiroyuki Sato 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 Hiroyuki Sato. Hiroyuki Sato 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.
Yoneyama, Tohru, Yuki Miura, Akio Yoneyama, et al.. (2025). The Effects of the Blood‐Based S2,3PSA% Test on Prostate Cancer Screening: A Novel Approach to Reduce the Need for MRI and Unnecessary Biopsies. The Prostate. 85(11). 1062–1068. 3 indexed citations
2.
Fukuda, Ellen H., et al.. (2024). Nonlinear conjugate gradient method for vector optimization on Riemannian manifolds with retraction and vector transport. Applied Mathematics and Computation. 486. 129001–129001. 1 indexed citations
3.
Sato, Hiroyuki. (2023). Riemannian optimization on unit sphere with p-norm and its applications. Computational Optimization and Applications. 85(3). 897–935. 3 indexed citations
4.
Sato, Hiroyuki, et al.. (2023). Conjugate Gradient Methods for Optimization Problems on Symplectic Stiefel Manifold. IEEE Control Systems Letters. 1–1. 1 indexed citations
5.
Sato, Hiroyuki, et al.. (2022). Self-Sovereign Identity as a Service: Architecture in Practice. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). 1536–1543. 8 indexed citations
6.
Sato, Hiroyuki, et al.. (2020). Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. Genome biology. 21(1). 9–9. 70 indexed citations
7.
Kasai, Hiroyuki, Hiroyuki Sato, & Bamdev Mishra. (2018). Riemannian Stochastic Recursive Gradient Algorithm. International Conference on Machine Learning. 2516–2524. 13 indexed citations
8.
Kasai, Hiroyuki, Hiroyuki Sato, & Bamdev Mishra. (2018). Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis.. International Conference on Machine Learning. 2521–2529. 1 indexed citations
9.
Sato, Hiroyuki, Hiroyuki Kasai, & Bamdev Mishra. (2017). Riemannian stochastic variance reduced gradient. arXiv (Cornell University). 6 indexed citations
10.
Sato, Hiroyuki. (2014). Riemannian conjugate gradient method for complex singular value decomposition problem. 5849–5854. 8 indexed citations
11.
Sato, Hiroyuki & Toshihiro Iwai. (2013). Convergence analysis for the Riemannian conjugate gradient method. arXiv (Cornell University). 1 indexed citations
12.
Mizobuchi, Ritsuko, Hiroyuki Sato, Shuichi Fukuoka, et al.. (2013). Mapping a quantitative trait locus for resistance to bacterial grain rot in rice. Rice. 6(1). 13–13. 24 indexed citations
13.
Furuya, Tomoko, Akira Matsunaga, Hiroshi Ideguchi, et al.. (2011). A CASE OF SUSPECTED PARTIAL D BECAUSE OF WEAK REACTIVITY TO ANTI-D ON COLUMN AGGLUTINATION TECHNOLOGY AND IDENTIFIED AS PARTIAL D (DBT-1) BY GENETIC TESTING. Japanese Journal of Transfusion and Cell Therapy. 57(4). 267–273.
14.
Takeuchi, Yoshinobu, Kiyosumi Hori, Keitaro Suzuki, et al.. (2008). Major QTLs for eating quality of an elite Japanese rice cultivar, Koshihikari, on the short arm of chromosome 3. Breeding Science. 58(4). 437–445. 56 indexed citations
15.
Sato, Hiroyuki, et al.. (2007). Karyotype analysis of a Japanese cucumber cultivar by fluorescence in situ hybridization. Chromosome science. 10(3). 65–69. 2 indexed citations
16.
Shimojo, Masataka, et al.. (2007). Introducing Viewpoints of Mechanics into Basic Growth Analysis (3) : Applying Growth Force and Leaf-Light Complex to Production and Digestion, Analyses of Forages. Journal of the Faculty of Agriculture Kyushu University. 52(1). 69–72. 2 indexed citations
17.
Sato, Hiroyuki. (2006). Socio-ecological Research of the Circular Settlements in Japanese Early Upper Paleolithic. 25. 267–281. 4 indexed citations
18.
Sato, Hiroyuki, et al.. (1998). A New Locus cnx 3 Involved in Molybdenum Cofactor Biosynthesis in Rice (Oryza sativa L.).. Ikushugaku zasshi. 48(2). 123–128. 1 indexed citations
19.
Sato, Hiroyuki, et al.. (1996). Characterization of four molybdenum cofactor mutants of rice, Oryza sativa L.. Plant Science. 119(1-2). 39–47. 24 indexed citations
20.
Mino, Yoshio, Toshihide Tsuda, Akira Babazono, et al.. (1993). Depressive States in Workers Using Computers. Environmental Research. 63(1). 54–59. 17 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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