World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
9758
World Ranking
12380
National Ranking
193

Motoaki Kawanabe 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 Motoaki Kawanabe 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: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Motoaki Kawanabe 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 Motoaki Kawanabe 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Motoaki Kawanabe is affiliated with the Advanced Telecommunications Research Institute International in Japan. Their research encompasses a wide range of topics within computer science and neuroscience, focusing on areas related to brain function and machine learning.

The main fields of study include:

  • Computer Science
  • Neuroscience

Key subfields Kawanabe has contributed to are:

  • Cognitive Neuroscience
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Radiology, Nuclear Medicine and Imaging

The principal topics explored in their work cover:

  • Functional Brain Connectivity Studies
  • Neural dynamics and brain function
  • EEG and Brain-Computer Interfaces
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Neural Networks and Applications

Kawanabe has published extensively, with the majority of papers appearing in venues such as:

  • arXiv (Cornell University)
  • NeuroImage
  • IEEE Access
  • bioRxiv (Cold Spring Harbor Laboratory)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Selected recent publications include:

  • "ScanQA: 3D Question Answering for Spatial Scene Understanding," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Asymmetric directed functional connectivity within the frontoparietal motor network during motor imagery and execution," 2021, NeuroImage
  • "Interpretable brain age prediction using linear latent variable models of functional connectivity," 2020, PLoS ONE
  • "Event-related microstate dynamics represents working memory performance," 2022, NeuroImage
  • "SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG," 2022, arXiv (Cornell University)

Frequent collaborators in Kawanabe's research include:

  • Taiki Miyanishi
  • Takeshi OGAWA
  • Jun-ichiro Hirayama
  • Reinmar J. Kobler
  • Shuhei Kurita

Best Publications

  • Optimizing Spatial filters for Robust EEG Single-Trial Analysis

    B. Blankertz;R. Tomioka;S. Lemm;M. Kawanabe

  • How to Explain Individual Classification Decisions

    David Baehrens;Timon Schroeter;Stefan Harmeling;Motoaki Kawanabe

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

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

  • Direct importance estimation for covariate shift adaptation

    Masashi Sugiyama;Taiji Suzuki;Shinichi Nakajima;Hisashi Kashima

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

    Masashi Sugiyama;Motoaki Kawanabe

  • Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces

    C Vidaurre;M Kawanabe;P von Bünau;B Blankertz

  • Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing

    Benjamin Blankertz;Motoaki Kawanabe;Ryota Tomioka;Friederike Hohlefeld

  • Stationary common spatial patterns for brain-computer interfacing.

    Wojciech Samek;Carmen Vidaurre;Klaus Robert Müller;Klaus Robert Müller;Motoaki Kawanabe

  • Optimal cluster preserving embedding of nonmetric proximity data

    V. Roth;J. Laub;M. Kawanabe;J.M. Buhmann

  • Divergence-Based Framework for Common Spatial Patterns Algorithms

    Wojciech Samek;Motoaki Kawanabe;Klaus-Robert Muller

  • A New Discriminative Kernel From Probabilistic Models

    Koji Tsuda;Motoaki Kawanabe;Gunnar Rätsch;Sören Sonnenburg

  • How to Explain Individual Classification Decisions

    David Baehrens;Timon Schroeter;Stefan Harmeling;Motoaki Kawanabe

  • Machine Learning in Non-Stationary Environments

    Unknown

  • Kernel-based nonlinear blind source separation

    Stefan Harmeling;Andreas Ziehe;Motoaki Kawanabe;Klaus-Robert Müller

  • A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

    F. Meinecke;A. Ziehe;M. Kawanabe;K.-R. Muller

  • Information geometry of estimating functions in semi-parametric statistical models

    Shun-Ichi Amari;Motoaki Kawanabe

  • Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG

    Stefan Haufe;Ryota Tomioka;Guido Nolte;Klaus-Robert Müller

  • Learning a common dictionary for subject-transfer decoding with resting calibration.

    Hiroshi Morioka;Atsunori Kanemura;Jun-ichiro Hirayama;Manabu Shikauchi

  • ScanQA: 3D Question Answering for Spatial Scene Understanding

    Unknown

  • Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation

    Andreas Ziehe;Motoaki Kawanabe;Stefan Harmeling;Klaus-Robert Müller

  • In Search of Non-Gaussian Components of a High-Dimensional Distribution

    Gilles Blanchard;Gilles Blanchard;Motoaki Kawanabe;Masashi Sugiyama;Masashi Sugiyama;Vladimir Spokoiny

  • On-line learning in changing environments with applications in supervised and unsupervised learning

    Noboru Murata;Motoaki Kawanabe;Andreas Ziehe;Klaus-Robert Müller

Frequent Co-Authors

Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Aapo Hyvärinen
Aapo Hyvärinen University of Helsinki
Carmen Vidaurre
Carmen Vidaurre Technical University of Berlin
Vladimir Spokoiny
Vladimir Spokoiny Weierstrass Institute for Applied Analysis and Stochastics
Koji Tsuda
Koji Tsuda University of Tokyo
Stefan Harmeling
Stefan Harmeling TU Dortmund University
Shun-ichi Amari
Shun-ichi Amari RIKEN Center for Brain Science
Benjamin Blankertz
Benjamin Blankertz Technical University of Berlin
Fabian J. Theis
Fabian J. Theis Technical University of Munich

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