World's Best Scientists 2026 revealed!
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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
59
Citations
18997
World Ranking
3360
National Ranking
29

Koji Tsuda 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 Koji Tsuda 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: 272 publications — 68th percentile

68% of scientists in this discipline score the same or lower.

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

Koji Tsuda 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 Koji Tsuda 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: 59 D-Index — 77th percentile

77% 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

Koji Tsuda is affiliated with the University of Tokyo in Japan. Their research spans multiple domains with a particular focus on computer science, materials science, and biochemistry, genetics, and molecular biology. This multidisciplinary approach is reflected in their main fields of study, which include:

  • Computer Science
  • Materials Science
  • Biochemistry, Genetics and Molecular Biology

Their work also extends into several subfields, emphasizing the intersection of computational techniques and biological or material systems. The subfields include:

  • Materials Chemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Tsuda has contributed to research on a variety of specialized topics, including:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Protein Structure and Dynamics
  • X-ray Diffraction in Crystallography
  • Monoclonal and Polyclonal Antibodies Research
  • Machine Learning and Data Classification
  • Quantum Computing Algorithms and Architecture

The scientist has published recent papers in leading academic venues. Notable papers include:

  • Designing metamaterials with quantum annealing and factorization machines (2020), Physical Review Research
  • Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks (2020), ACS Omega
  • Black-Box Optimization for Automated Discovery (2021), Accounts of Chemical Research
  • Bayesian optimization package: PHYSBO (2022), Computer Physics Communications
  • Machine learning-driven optimization in powder manufacturing of Ni-Co based superalloy (2020), Materials & Design

Frequently appearing collaborators in Tsuda's publications include:

  • Ryo Tamura
  • Kei Terayama
  • Masato Sumita
  • Andrejs Tučs
  • Adnan Sljoka

The publication record also shows regular appearances in several key academic venues, including:

  • arXiv (Cornell University)
  • Science and Technology of Advanced Materials Methods
  • Scientific Reports
  • Digital Discovery
  • Journal of Chemical Theory and Computation

Best Publications

  • An introduction to kernel-based learning algorithms

    K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda

  • Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

    Andrejs Tucs;Duy Phuoc Tran;Akiko Yumoto;Yoshihiro Ito

  • Marginalized kernels between labeled graphs

    Hisashi Kashima;Koji Tsuda;Akihiro Inokuchi

  • Kernel Methods in Computational Biology

    Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert

  • A Primer on Kernel Methods

    JP Vert;K Tsuda;B Schölkopf;B. Schölkopf K. Tsuda

  • Support Vector Machine Applications in Computational Biology

    Bernhard Schölkopf;Koji Tsuda;Jean-Philippe Vert

  • COMBO: An efficient Bayesian optimization library for materials science

    Tsuyoshi Ueno;Trevor David Rhone;Zhufeng Hou;Teruyasu Mizoguchi

  • Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

    Atsuto Seko;Tomoya Maekawa;Koji Tsuda;Isao Tanaka

  • Fast protein classification with multiple networks

    Koji Tsuda;Hyunjung Shin;Bernhard Schölkopf

  • ChemTS: an efficient python library for de novo molecular generation

    Xiufeng Yang;Jinzhe Zhang;Kazuki Yoshizoe;Kei Terayama

  • Marginalized kernels for biological sequences.

    Koji Tsuda;Taishin Kin;Kiyoshi Asai

  • Discriminative Subsequence Mining for Action Classification

    S. Nowozin;G. Bakir;K. Tsuda

  • Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

    Koji Tsuda;Gunnar Rätsch;Manfred K. Warmuth

  • A New Discriminative Kernel From Probabilistic Models

    Koji Tsuda;Motoaki Kawanabe;Gunnar Rätsch;Sören Sonnenburg

  • Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction

    Hisashi Kashima;Tsuyoshi Kato;Yoshihiro Yamanishi;Masashi Sugiyama

  • Crystal structure prediction accelerated by Bayesian optimization

    Tomoki Yamashita;Tomoki Yamashita;Nobuya Sato;Hiori Kino;Takashi Miyake;Takashi Miyake

  • Machine-learning prediction of the d-band center for metals and bimetals

    Ichigaku Takigawa;Ichigaku Takigawa;Ken-ichi Shimizu;Ken-ichi Shimizu;Koji Tsuda;Koji Tsuda;Koji Tsuda;Satoru Takakusagi

  • Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

    Yutaka Saito;Misaki Oikawa;Hikaru Nakazawa;Teppei Niide

  • Kernels for graphs

    H Kashima;K Tsuda;A Inokuchi;B. Schoelkopf K. Tsuda

  • gBoost: a mathematical programming approach to graph classification and regression

    Hiroto Saigo;Sebastian Nowozin;Tadashi Kadowaki;Taku Kudo

  • Learning kernels from biological networks by maximizing entropy

    K Tsuda;WS Noble

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Kiyoshi Asai
Kiyoshi Asai University of Tokyo
Junichiro Shiomi
Junichiro Shiomi University of Tokyo
Jean-Philippe Vert
Jean-Philippe Vert Google (United States)
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Hisashi Kashima
Hisashi Kashima Kyoto University
Takeaki Uno
Takeaki Uno National Institute of Informatics
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin

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