World's Best Scientists 2026 revealed!
Naonori Ueda

Naonori Ueda

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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
48
Citations
9635
World Ranking
6164
National Ranking
86

Naonori Ueda 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 Naonori Ueda 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: 254 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Naonori Ueda 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 Naonori Ueda 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: 48 D-Index — 58th percentile

58% 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

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

Overview

Naonori Ueda is affiliated with RIKEN in Japan and has contributed extensively to medical research, particularly in the fields of oncology, surgery, and otorhinolaryngology. Their scientific work spans diverse topics related to cancer diagnosis and treatment, head and neck cancer, bone health and treatments, and oral health in cancer treatment.

Their research has extensively covered areas including:

  • Cancer Diagnosis and Treatment
  • Head and Neck Cancer Studies
  • Bone health and treatments
  • Bone and Joint Diseases
  • Oral health in cancer treatment
  • Reconstructive Surgery and Microvascular Techniques
  • Head and Neck Surgical Oncology

Published work by Naonori Ueda appears frequently in medical journals such as the Journal of Oral and Maxillofacial Surgery Medicine and Pathology, the Japanese Journal of Oral & Maxillofacial Surgery, Osteoporosis International, Journal of Dental Sciences, and Medicine.

Some of the recent papers authored or coauthored by Ueda include:

  • Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images, 2020, IEEE Access
  • Prevalence of and risk factors for postoperative complications after lower third molar extraction: A multicenter prospective observational study in Japan, 2022, Medicine
  • Denosumab-related osteonecrosis of the jaw after tooth extraction and the effects of a short drug holiday in cancer patients: a multicenter retrospective study, 2021, Osteoporosis International
  • Oral risk factors associated with medication-related osteonecrosis of the jaw in patients with cancer, 2021, Journal of Bone and Mineral Metabolism
  • Risk factors for osteoradionecrosis of the jaw in patients with oral or oropharyngeal cancer: Verification of the effect of tooth extraction before radiotherapy using propensity score matching analysis, 2021, Journal of Dental Sciences

Naonori Ueda has collaborated with several frequent coauthors, highlighting key partnerships within their research network. These coauthors include:

  • Tadaaki Kirita
  • Hiroshi Kurita
  • Nobuhiro Yamakawa
  • Shin-ichi Yamada
  • Takumi Hasegawa

Their main field of study is medicine, with 70 publications, and subfields include oncology and surgery with 19 publications each, otorhinolaryngology with 11, pulmonary and respiratory medicine with 6, and oral surgery with 5 publications.

Best Publications

  • Learning systems of concepts with an infinite relational model

    Charles Kemp;Joshua B. Tenenbaum;Thomas L. Griffiths;Takeshi Yamada

  • Deterministic annealing EM algorithm

    Naonori Ueda;Ryohei Nakano

  • SMEM Algorithm for Mixture Models

    Naonori Ueda;Ryohei Nakano;Zoubin Ghahramani;Geoffrey E. Hinton

  • Parametric Mixture Models for Multi-Labeled Text

    Naonori Ueda;Kazumi Saito

  • Generalization error of ensemble estimators

    N. Ueda;R. Nakano

  • Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

    H. Sawada;H. Kameoka;S. Araki;N. Ueda

  • Bayesian Unsupervised Word Segmentation with Nested Pitman-Yor Language Modeling

    Daichi Mochihashi;Takeshi Yamada;Naonori Ueda

  • Optimal linear combination of neural networks for improving classification performance

    N. Ueda

  • Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates

    Naonori Ueda;Ryohei Nakano;Zoubin Ghahramani;Geoffrey E. Hinton

  • Land cover classification from fused DSM and UAV images using convolutional neural networks

    Husam Abdulrasool H. Al-Najjar;Bahareh Kalantar;Biswajeet Pradhan;Vahideh Saeidi

  • Topic tracking model for analyzing consumer purchase behavior

    Tomoharu Iwata;Shinji Watanabe;Takeshi Yamada;Naonori Ueda

  • Bayesian model search for mixture models based on optimizing variational bounds

    Naonori Ueda;Zoubin Ghahramani

  • Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data

    Bahareh Kalantar;Naonori Ueda;Mohammed Oludare Idrees;Saeid Janizadeh

  • A hybrid generative/discriminative approach to semi-supervised classifier design

    Akinori Fujino;Naonori Ueda;Kazumi Saito

  • Online multiscale dynamic topic models

    Tomoharu Iwata;Takeshi Yamada;Yasushi Sakurai;Naonori Ueda

  • Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data

    Bahareh Kalantar;Naonori Ueda;Vahideh Saeidi;Kourosh Ahmadi

  • Higher-order factorization machines

    Mathieu Blondel;Akinori Fujino;Naonori Ueda;Masakazu Ishihata

  • Learning visual models from shape contours using multiscale convex/concave structure matching

    N. Ueda;S. Suzuki

  • A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers

    Naonori Ueda;Ryohei Nakano

  • Probabilistic latent semantic visualization: topic model for visualizing documents

    Tomoharu Iwata;Takeshi Yamada;Naonori Ueda

  • Variational bayesian estimation and clustering for speech recognition

    S. Watanabe;Y. Minami;A. Nakamura;N. Ueda

Frequent Co-Authors

Hiroshi Sawada
Hiroshi Sawada NTT (Japan)
Masaomi Tanaka
Masaomi Tanaka Tohoku University
Naoki Yoshida
Naoki Yoshida University of Tokyo
Nozomu Tominaga
Nozomu Tominaga Konan University
Naoki Yasuda
Naoki Yasuda Kavli Institute for the Physics and Mathematics of the Universe
Tomoki Morokuma
Tomoki Morokuma Chiba Institute of Technology
Hisashi Kashima
Hisashi Kashima Kyoto University
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Shinji Watanabe
Shinji Watanabe Carnegie Mellon University
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto

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