World's Best Scientists 2026 revealed!
Thomas Villmann

Thomas Villmann

D-Index & Metrics

Computer Science

D-Index
36
Citations
6530
World Ranking
11138
National Ranking
559

Thomas Villmann 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 Thomas Villmann 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: 325 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Thomas Villmann 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 Thomas Villmann 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: 36 D-Index — 23rd percentile

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

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

Overview

Thomas Villmann is affiliated with Hochschule Mittweida in Germany and has made contributions primarily in the fields of Computer Science and Biochemistry, Genetics and Molecular Biology. Their research focuses extensively on Artificial Intelligence and Molecular Biology, with additional work in Computer Vision and Pattern Recognition, Control and Systems Engineering, and Computational Theory and Mathematics.

The scientist's main topics of work concentrate on Neural Networks and Applications, Machine Learning in Bioinformatics, Adversarial Robustness in Machine Learning, Machine Learning and Data Classification, Fractal and DNA sequence analysis, Anomaly Detection Techniques and Applications, and Fault Detection and Control Systems.

Frequent publication venues include:

  • Neurocomputing
  • ESANN 2021 proceedings
  • Neural Computing and Applications
  • Entropy
  • bioRxiv (Cold Spring Harbor Laboratory)

Several recent papers illustrate their research outputs, such as:

  • "The coming of age of interpretable and explainable machine learning models" (2023) published in Neurocomputing
  • "Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective" (2022) published in IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • "The Coming of Age of Interpretable and Explainable Machine Learning Models" (2021) published in ESANN 2021 proceedings
  • "Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences" (2021) published in Neural Computing and Applications
  • "Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability" (2020) published in Neurocomputing

Thomas Villmann collaborates frequently with several coauthors, including Marika Kaden, Sascha Saralajew, Katrin Sophie Bohnsack, Jensun Ravichandran, and Alexander Engelsberger. These collaborations have contributed to a consistent output of publications in key venues within their fields of study.

Best Publications

  • Generalized relevance learning vector quantization

    Barbara Hammer;Thomas Villmann

  • Topology preservation in self-organizing feature maps: exact definition and measurement

    T. Villmann;R. Der;M. Herrmann;T.M. Martinetz

  • Neural maps in remote sensing image analysis

    Thomas Villmann;Erzsébet Merényi;Barbara Hammer

  • Serotonin and dopamine transporter imaging in patients with obsessive-compulsive disorder.

    Swen Hesse;Ulrich Müller;Ulrich Müller;Thomas Lincke;Henryk Barthel

  • Growing a hypercubical output space in a self-organizing feature map

    H.-U. Bauer;T. Villmann

  • Supervised Neural Gas with General Similarity Measure

    Barbara Hammer;Marc Strickert;Thomas Villmann

  • Batch and median neural gas

    Marie Cottrell;Barbara Hammer;Alexander Hasenfuß;Thomas Villmann

  • Prototype-based models in machine learning.

    Michael Biehl;Barbara Hammer;Thomas Villmann

  • Neural maps and topographic vector quantization

    H.-U. Bauer;M. Herrmann;T. Villmann

  • Limited Rank Matrix Learning, discriminative dimension reduction and visualization

    Kerstin Bunte;Petra Schneider;Barbara Hammer;Frank-Michael Schleif

  • Vector Quantization by Optimal Neural Gas

    M. Herrmann;Thomas Villmann

  • Regularization in Matrix Relevance Learning

    Petra Schneider;Kerstin Bunte;Han Stiekema;Barbara Hammer

  • Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences

    Kerstin Bunte;Sven Haase;Michael Biehl;Thomas Villmann

  • On the Generalization Ability of GRLVQ Networks

    Barbara Hammer;Marc Strickert;Thomas Villmann

  • Divergence-based vector quantization

    Thomas Villmann;Sven Haase

  • Magnification Control in Self-Organizing Maps and Neural Gas

    Thomas Villmann;Jens Christian Claussen

  • Computational aspects of inverse analyses for determining softening curves of concrete

    Volker Slowik;Beate Villmann;Nick Bretschneider;Thomas Villmann

  • Divergence-based classification in learning vector quantization

    E. Mwebaze;P. Schneider;F. M. Schleif;J. R. Aduwo

  • Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

    M. Kaden;M. Lange;D. Nebel;M. Riedel

  • Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

    Thomas Villmann;Andrea Bohnsack;Marika Kaden

Frequent Co-Authors

Barbara Hammer
Barbara Hammer Bielefeld University
Jacek Blazewicz
Jacek Blazewicz Poznań University of Technology
Michel Verleysen
Michel Verleysen Université Catholique de Louvain
Axel Wismüller
Axel Wismüller University of Rochester
Henryk Barthel
Henryk Barthel Leipzig University
Thomas Martinetz
Thomas Martinetz University of Lübeck
Sepp Hochreiter
Sepp Hochreiter Johannes Kepler University of Linz
Nese Sreenivasulu
Nese Sreenivasulu International Rice Research Institute
Andreas Zell
Andreas Zell University of Tübingen
Alessandro Sperduti
Alessandro Sperduti University of Padua

External Links

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