World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
11796
World Ranking
5294
National Ranking
246

Thomas Seidl 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 Seidl 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: 402 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Thomas Seidl 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 Seidl 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Thomas Seidl is affiliated with Ludwig-Maximilians-Universität München in Germany. Their research contributions span primarily the field of Computer Science, with a focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and Statistical and Nonlinear Physics.

The scientist's publication record includes a notable number of papers in key venues, with a significant presence in arXiv (Cornell University) where they have published 23 works. Other frequent publication venues include the 2021 International Conference on Data Mining Workshops (ICDMW), Proceedings of the AAAI Conference on Artificial Intelligence, Datenbank-Spektrum, and Rock Mechanics and Rock Engineering.

Thomas Seidl's recent papers demonstrate a range of interests and topics. Selected papers include:

  • "Blastability and Ore Grade Assessment from Drill Monitoring for Open Pit Applications" (2021, Rock Mechanics and Rock Engineering)
  • "Argument Mining Driven Analysis of Peer-Reviews" (2021, Proceedings of the AAAI Conference on Artificial Intelligence)
  • "InstanceFormer: An Online Video Instance Segmentation Framework" (2023, Proceedings of the AAAI Conference on Artificial Intelligence)
  • "Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study" (2022, Advances in Data Analysis and Classification)
  • "VR Based Visualization and Exploration of Plant Biological Data" (2024, Fraunhofer-Publica (Fraunhofer-Gesellschaft))

The scientist frequently collaborates with other researchers, with their most common coauthors being Matthias Schubert, Anna Beer, Florian Richter, Tanveer Hannan, and Ludwig Zellner. The counts associated with these collaborations range from eight to eleven joint publications per coauthor.

Thomas Seidl's research covers various topics within computer science, including:

  • Anomaly Detection Techniques and Applications
  • Advanced Clustering Algorithms Research
  • Topic Modeling
  • Data Stream Mining Techniques
  • Machine Learning and Algorithms
  • Data Management and Algorithms
  • Software Engineering Research

Best Publications

  • MOA: Massive Online Analysis, a framework for stream classification and clustering.

    Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Philipp Kranen

  • 3D Shape Histograms for Similarity Search and Classification in Spatial Databases

    Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl

  • Optimal multi-step k-nearest neighbor search

    Thomas Seidl;Hans-Peter Kriegel

  • Evaluating Clustering in Subspace Projections of High Dimensional Data

    Emmanuel Müller;Stephan Günnemann;Ira Assent;Thomas Seidl

  • The ClusTree: indexing micro-clusters for anytime stream mining

    Philipp Kranen;Ira Assent;Corinna Baldauf;Thomas Seidl

  • Efficient User-Adaptable Similarity Search in Large Multimedia Databases

    Thomas Seidl;Hans-Peter Kriegel

  • Fast nearest neighbor search in high-dimensional space

    S. Berchtold;B. Ertl;D.A. Keim;H.-P. Kriegel

  • Nearest Neighbor Classification in 3D Protein Databases

    Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl

  • Managing Intervals Efficiently in Object-Relational Databases

    Hans-Peter Kriegel;Marco Pötke;Thomas Seidl

  • DUSC: Dimensionality Unbiased Subspace Clustering

    I. Assent;R. Krieger;E. Muller;T. Seidl

  • Statistical selection of relevant subspace projections for outlier ranking

    Emmanuel Muller;Matthias Schiffer;Thomas Seidl

  • Mining coherent subgraphs in multi-layer graphs with edge labels

    Brigitte Boden;Stephan Günnemann;Holger Hoffmann;Thomas Seidl

  • On Using Class-Labels in Evaluation of Clusterings

    Ines Färber;Stephan Günnemann;Hans-Peter Kriegel;Peer Kröger

  • Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms

    Stephan Gunnemann;Ines Farber;Brigitte Boden;Thomas Seidl

  • Clicks: An effective algorithm for mining subspace clusters in categorical datasets

    Mohammed J. Zaki;Markus Peters;Ira Assent;Thomas Seidl

  • Signature Quadratic Form Distance

    Christian Beecks;Merih Seran Uysal;Thomas Seidl

  • Subspace search and visualization to make sense of alternative clusterings in high-dimensional data

    Andrada Tatu;Fabian Maas;Ines Farber;Enrico Bertini

  • INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy

    I. Assent;R. Krieger;E. Muller;T. Seidl

  • An effective evaluation measure for clustering on evolving data streams

    Hardy Kremer;Philipp Kranen;Timm Jansen;Thomas Seidl

  • AnyOut: anytime outlier detection on streaming data

    Ira Assent;Philipp Kranen;Corinna Baldauf;Thomas Seidl

Frequent Co-Authors

Ira Assent
Ira Assent Aarhus University
Stephan Günnemann
Stephan Günnemann Technical University of Munich
Hans-Peter Kriegel
Hans-Peter Kriegel Ludwig-Maximilians-Universität München
Peer Kröger
Peer Kröger Kiel University
Daniel A. Keim
Daniel A. Keim University of Konstanz
Rik Van de Walle
Rik Van de Walle Ghent University
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Albert Bifet
Albert Bifet University of Waikato
Volker Tresp
Volker Tresp Ludwig-Maximilians-Universität München
Nikos Mamoulis
Nikos Mamoulis University of Ioannina

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