World's Best Scientists 2026 revealed!
Masashi Sugiyama

Masashi Sugiyama

Award Badge
Computer Science
Japan
2026

D-Index & Metrics

Computer Science

D-Index
81
Citations
30891
World Ranking
1003
National Ranking
3

Masashi Sugiyama publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Masashi Sugiyama sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 810 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Masashi Sugiyama D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Masashi Sugiyama sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 81 D-Index — 93rd percentile

93% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Computer Science in Japan Leader Award
  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Masashi Sugiyama is affiliated with RIKEN in Japan and specializes in the field of computer science, with a significant focus on artificial intelligence. Their research spans several subfields including computer vision and pattern recognition, management science and operations research, computer networks and communications, and civil and structural engineering.

The scientist's work extensively covers themes related to machine learning and data classification, domain adaptation and few-shot learning, and machine learning algorithms. Other prominent topics include anomaly detection techniques and applications, adversarial robustness in machine learning, imbalanced data classification techniques, and advanced bandit algorithms research.

Masashi Sugiyama has a strong record of publications in a variety of venues:

  • arXiv (Cornell University)
  • Neural Computation
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Neural Networks
  • Zenodo (CERN European Organization for Nuclear Research)

Recent papers authored or co-authored by Sugiyama include:

  • "Deep learning, reinforcement learning, and world models", 2022, published in Neural Networks
  • "Part-dependent Label Noise: Towards Instance-dependent Label Noise", 2020, published in arXiv (Cornell University)
  • "Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review", 2024, published in Journal of Medical Systems
  • "Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning", 2020, published in arXiv (Cornell University)
  • "A Survey of Label-noise Representation Learning: Past, Present and Future", 2020, published in arXiv (Cornell University)

Sugiyama collaborates frequently with several researchers. The main frequent co-authors include:

  • Gang Niu
  • Tongliang Liu
  • Bo Han
  • Jingfeng Zhang

Best Publications

  • Dataset Shift in Machine Learning

    Joaquin Quionero-Candela;Masashi Sugiyama;Anton Schwaighofer;Neil D. Lawrence

  • Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

    Bo Han;Quanming Yao;Xingrui Yu;Gang Niu

  • Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

    Masashi Sugiyama

  • Covariate Shift Adaptation by Importance Weighted Cross Validation

    Masashi Sugiyama;Matthias Krauledat;Klaus-Robert Müller

  • Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

    Masashi Sugiyama;Shinichi Nakajima;Hisashi Kashima;Paul V. Buenau

  • Covariate Shift by Kernel Mean Matching

    A Gretton;AJ Smola;J Huang;M Schmittfull

  • Direct importance estimation for covariate shift adaptation

    Masashi Sugiyama;Taiji Suzuki;Shinichi Nakajima;Hisashi Kashima

  • Change-point detection in time-series data by relative density-ratio estimation

    Song Liu;Makoto Yamada;Nigel Collier;Masashi Sugiyama

  • Density Ratio Estimation in Machine Learning

    Masashi Sugiyama;Taiji Suzuki;Takafumi Kanamori

  • Deep learning, reinforcement learning, and world models

    Unknown

  • A Least-squares Approach to Direct Importance Estimation

    Takafumi Kanamori;Shohei Hido;Masashi Sugiyama

  • Local Fisher discriminant analysis for supervised dimensionality reduction

    Masashi Sugiyama

  • How does disagreement help generalization against label corruption

    Xingrui Yu;Bo Han;Jiangchao Yao;Gang Niu

  • Semi-supervised local Fisher discriminant analysis for dimensionality reduction

    Masashi Sugiyama;Tsuyoshi Idé;Shinichi Nakajima;Jun Sese

  • High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

    Makoto Yamada;Wittawat Jitkrittum;Leonid Sigal;Eric P. Xing

  • Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

    Masashi Sugiyama;Motoaki Kawanabe

  • Positive-Unlabeled Learning with Non-Negative Risk Estimator

    Ryuichi Kiryo;Gang Niu;Marthinus Christoffel du Plessis;Masashi Sugiyama

  • Learning discrete representations via information maximizing self-augmented training

    Weihua Hu;Takeru Miyato;Seiya Tokui;Eiichi Matsumoto

  • Analysis of Learning from Positive and Unlabeled Data

    Marthinus C du Plessis;Gang Niu;Masashi Sugiyama

  • Active Learning in Recommender Systems

    Neil Rubens;Dain Kaplan;Masashi Sugiyama

  • Convex Formulation for Learning from Positive and Unlabeled Data

    Marthinus Du Plessis;Gang Niu;Masashi Sugiyama

Frequent Co-Authors

Taiji Suzuki
Taiji Suzuki University of Tokyo
Motoaki Kawanabe
Motoaki Kawanabe Advanced Telecommunications Research Institute International
Hisashi Kashima
Hisashi Kashima Kyoto University
Ryota Tomioka
Ryota Tomioka Microsoft (United States)
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research
Neil D. Lawrence
Neil D. Lawrence University of Cambridge
Tongliang Liu
Tongliang Liu University of Sydney
Alexandra J. Golby
Alexandra J. Golby Brigham and Women's Hospital

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online study options in Computer Science opens diverse pathways. If you’re looking for quick ways to boost your resume, consider easy certifications that pay well. Many of these credentials can be completed online and offer practical, job-ready skills that employers value.

Those seeking more advanced qualifications might consider fastest masters degree online programs. These allow you to accelerate your studies and advance your career without spending years in school.

Wondering what masters program should I do? It’s important to align your choice with current industry trends and your long-term goals. In-demand programs often lead to better employment prospects and higher earnings.

For those new to higher education, associate's degree online options provide a flexible starting point. These programs can lead straight into entry-level tech jobs or serve as a stepping stone to bachelor’s and graduate-level study.

Best Scientists Citing Masashi Sugiyama

Trending Scientists

Recently Published Articles