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Computer Science
Germany
2026

D-Index & Metrics

Computer Science

D-Index
161
Citations
140992
World Ranking
21
National Ranking
3

Klaus-Robert Müller 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 Klaus-Robert Müller 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 753 publications — 98th percentile

98% of scientists in this discipline score the same or lower.

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

Klaus-Robert Müller 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 Klaus-Robert Müller sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 161 D-Index — 100th percentile

100% 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 Germany Leader Award
  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Klaus-Robert Müller is a researcher affiliated with the Technical University of Berlin in Germany. Their academic focus spans multiple fields with an emphasis on artificial intelligence and its applications in medicine and materials science.

Their recent works include a range of publications addressing explainable artificial intelligence, machine learning methods, and medical imaging. Notable papers are:

  • Higher-Order Explanations of Graph Neural Networks via Relevant Walks, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Mammography Image Quality Assurance Using Deep Learning, 2020, IEEE Transactions on Biomedical Engineering
  • Fairwashing Explanations with Off-Manifold Detergent, 2020, arXiv (Cornell University)
  • Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology, 2021, Preprints.org
  • Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence, 2025, Nature Cancer

Their research contributions cover several key fields of study:

  • Computer Science
  • Medicine

Within these fields, subfields that receive particular attention include:

  • Artificial Intelligence
  • Materials Chemistry
  • Oncology
  • Radiology, Nuclear Medicine and Imaging
  • Computational Theory and Mathematics

Müller's work addresses various specialized topics including:

  • Explainable Artificial Intelligence (XAI)
  • Machine Learning in Materials Science
  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Computational Drug Discovery Methods
  • Cancer Treatment and Pharmacology
  • Machine Learning and Data Classification

Their frequent coauthors include:

  • Grégoire Montavon
  • Shinichi Nakajima
  • Frederick Klauschen
  • Lukas Ruff
  • Gabriel Dernbach

Publications by Klaus-Robert Müller are commonly found in the following venues:

  • arXiv (Cornell University)
  • Annals of Oncology
  • ESMO Open
  • Chemical Science
  • Nature Communications

Best Publications

  • Nonlinear component analysis as a kernel eigenvalue problem

    Bernhard Schölkopf;Alexander Smola;Klaus-Robert Müller

  • An introduction to kernel-based learning algorithms

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

  • On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.

    Sebastian Bach;Alexander Binder;Grégoire Montavon;Frederick Klauschen

  • Efficient BackProp

    Yann LeCun;Léon Bottou;Genevieve B. Orr;Klaus-Robert Müller

  • Efficient BackProp

    Unknown

  • Kernel Principal Component Analysis

    Bernhard Schölkopf;Alex J. Smola;Klaus-Robert Müller

  • Methods for interpreting and understanding deep neural networks

    Grégoire Montavon;Wojciech Samek;Klaus Robert Müller;Klaus Robert Müller;Klaus Robert Müller

  • Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

    Matthias Rupp;Matthias Rupp;Alexandre Tkatchenko;Alexandre Tkatchenko;Klaus Robert Müller;Klaus Robert Müller;O. Anatole Von Lilienfeld;O. Anatole Von Lilienfeld

  • Optimizing Spatial filters for Robust EEG Single-Trial Analysis

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

  • SchNet - A deep learning architecture for molecules and materials.

    Kristof T. Schütt;Huziel E. Sauceda;P. J. Kindermans;Alexandre Tkatchenko

  • Soft Margins for AdaBoost

    G. Rätsch;T. Onoda;K.-R. Müller

  • Input space versus feature space in kernel-based methods

    B. Scholkopf;S. Mika;C.J.C. Burges;P. Knirsch

  • Explaining nonlinear classification decisions with deep Taylor decomposition

    Grégoire Montavon;Sebastian Lapuschkin;Alexander Binder;Wojciech Samek

  • Predicting Time Series with Support Vector Machines

    Klaus-Robert Müller;Alex J. Smola;Gunnar Rätsch;Bernhard Schölkopf

  • A Unifying Review of Deep and Shallow Anomaly Detection

    Lukas Ruff;Jacob R. Kauffmann;Robert A. Vandermeulen;Gregoire Montavon

  • Kernel PCA and De-Noising in Feature Spaces

    Sebastian Mika;Bernhard Schölkopf;Alex J. Smola;Klaus-Robert Müller

  • Single-Trial Analysis and Classification of ERP Components - a Tutorial

    Benjamin Blankertz;Steven Lemm;Matthias Sebastian Treder;Stefan Haufe

  • Unmasking Clever Hans predictors and assessing what machines really learn.

    Sebastian Lapuschkin;Stephan Wäldchen;Alexander Binder;Grégoire Montavon

  • Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models

    Wojciech Samek;Thomas Wiegand;Klaus-Robert Müller

  • Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

    Felix Sattler;Simon Wiedemann;Klaus-Robert Muller;Wojciech Samek

  • Evaluating the Visualization of What a Deep Neural Network Has Learned

    Wojciech Samek;Alexander Binder;Gregoire Montavon;Sebastian Lapuschkin

  • The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.

    Benjamin Blankertz;Guido Dornhege;Matthias Krauledat;Klaus Robert Müller

  • The BCI competition III: validating alternative approaches to actual BCI problems

    B. Blankertz;K.-R. Muller;D.J. Krusienski;G. Schalk

  • Robust and Communication-Efficient Federated Learning from Non-IID Data

    Felix Sattler;Simon Wiedemann;Klaus-Robert Müller;Wojciech Samek

Frequent Co-Authors

Benjamin Blankertz
Benjamin Blankertz Technical University of Berlin
Grégoire Montavon
Grégoire Montavon Freie Universität Berlin
Wojciech Samek
Wojciech Samek Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
Thomas Wiegand
Thomas Wiegand Technical University of Berlin
Motoaki Kawanabe
Motoaki Kawanabe Advanced Telecommunications Research Institute International
Alexandre Tkatchenko
Alexandre Tkatchenko University of Luxembourg
Gabriel Curio
Gabriel Curio Charité - University Medicine Berlin
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
José del R. Millán
José del R. Millán The University of Texas at Austin
Matthias Rupp
Matthias Rupp Luxembourg Institute of Science and Technology

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