World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
9841
World Ranking
9115
National Ranking
2544

Ryota Tomioka publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Ryota Tomioka sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 94 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Ryota Tomioka D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Ryota Tomioka sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 34 D-Index — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Ryota Tomioka is affiliated with Microsoft in the United States and specializes in research at the intersection of computer science and materials chemistry. Their body of work spans diverse subfields, including artificial intelligence, molecular biology, hardware and architecture, and computer vision and pattern recognition.

The scientist's research primarily focuses on machine learning applications within materials science, protein structure and dynamics, and techniques related to parallel computing and optimization. Their work also explores advanced neural network applications, advanced data storage technologies, adversarial robustness in machine learning, and X-ray diffraction in crystallography.

Ryota Tomioka has contributed to several recent publications, including:

  • A generative model for inorganic materials design, 2025, Nature
  • MatterGen: a generative model for inorganic materials design, 2023, arXiv (Cornell University)
  • Scalable emulation of protein equilibrium ensembles with generative deep learning, 2025, Science
  • On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them, 2020, arXiv (Cornell University)
  • Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics, 2023, arXiv (Cornell University)

The researcher frequently publishes in prominent venues, such as:

  • arXiv (Cornell University)
  • Nature
  • Science
  • Annals of Internal Medicine

Frequent collaborators include Claudio Zeni, Robert Pinsler, Daniel Zügner, Matthew K. Horton, and Jonathan Crabbé. Each of these coauthors has contributed to multiple publications alongside Ryota Tomioka.

Best Publications

  • Optimizing Spatial filters for Robust EEG Single-Trial Analysis

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

  • f -GAN: training generative neural samplers using variational divergence minimization

    Sebastian Nowozin;Botond Cseke;Ryota Tomioka

  • QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

    Dan Alistarh;Demjan Grubic;Jerry Z. Li;Ryota Tomioka

  • Norm-Based Capacity Control in Neural Networks

    Behnam Neyshabur;Ryota Tomioka;Nathan Srebro

  • In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

    Behnam Neyshabur;Ryota Tomioka;Nathan Srebro

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

    Benjamin Blankertz;Motoaki Kawanabe;Ryota Tomioka;Friederike Hohlefeld

  • Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

    Diane Bouchacourt;Ryota Tomioka;Sebastian Nowozin

  • Estimation of low-rank tensors via convex optimization

    Ryota Tomioka;Kohei Hayashi;Hisashi Kashima

  • Tensor factorization using auxiliary information

    Atsuhiro Narita;Kohei Hayashi;Ryota Tomioka;Hisashi Kashima

  • A regularized discriminative framework for EEG analysis with application to brain-computer interface.

    Ryota Tomioka;Ryota Tomioka;Ryota Tomioka;Klaus Robert Müller

  • Statistical Performance of Convex Tensor Decomposition

    Ryota Tomioka;Taiji Suzuki;Kohei Hayashi;Hisashi Kashima

  • Geometry of Optimization and Implicit Regularization in Deep Learning.

    Behnam Neyshabur;Ryota Tomioka;Ruslan Salakhutdinov;Nathan Srebro

  • Logistic Regression for Single Trial EEG Classification

    Ryota Tomioka;Kazuyuki Aihara;Klaus-Robert Müller

  • Discovering Emerging Topics in Social Streams via Link-Anomaly Detection

    Toshimitsu Takahashi;Ryota Tomioka;Kenji Yamanishi

  • Large-scale EEG/MEG source localization with spatial flexibility.

    Stefan Haufe;Ryota Tomioka;Thorsten Dickhaus;Claudia Sannelli

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

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

  • Convex Tensor Decomposition via Structured Schatten Norm Regularization

    Ryota Tomioka;Taiji Suzuki

  • Global analytic solution of fully-observed variational Bayesian matrix factorization

    Shinichi Nakajima;Masashi Sugiyama;S. Derin Babacan;Ryota Tomioka

  • The algebraic combinatorial approach for low-rank matrix completion

    Franz J. Király;Louis Theran;Ryota Tomioka

  • Multivariate analysis of noise in genetic regulatory networks

    Ryota Tomioka;Hidenori Kimura;Tetsuya J. Kobayashi;Kazuyuki Aihara

  • QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent

    Dan Alistarh;Jerry Li;Ryota Tomioka;Milan Vojnovic

  • Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

    Emile Mathieu;Charline Le Lan;Chris J. Maddison;Ryota Tomioka

  • QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks

    Dan Alistarh;Demjan Grubic;Jerry Li;Ryota Tomioka

Frequent Co-Authors

Taiji Suzuki
Taiji Suzuki University of Tokyo
Hisashi Kashima
Hisashi Kashima Kyoto University
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Behnam Neyshabur
Behnam Neyshabur New York University
Nathan Srebro
Nathan Srebro Toyota Technological Institute at Chicago
Dan Alistarh
Dan Alistarh Institute of Science and Technology Austria
Milan Vojnovic
Milan Vojnovic London School of Economics and Political Science
Kazuyuki Aihara
Kazuyuki Aihara University of Tokyo
Guido Nolte
Guido Nolte Universität Hamburg

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