World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
17989
World Ranking
2731
National Ranking
119

Volker Tresp 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 Volker Tresp 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: 355 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Volker Tresp 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 Volker Tresp 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: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Overview

Volker Tresp is affiliated with Ludwig-Maximilians-Universität München in Germany. Their research predominantly falls within the field of Computer Science, with a significant focus on Artificial Intelligence. The breadth of their work also covers subfields including Computer Vision and Pattern Recognition, Molecular Biology, Management Science and Operations Research, and Information Systems.

The scientist's research interests span multiple advanced topics, which include:

  • Topic Modeling
  • Advanced Graph Neural Networks
  • Multimodal Machine Learning Applications
  • Data Quality and Management
  • Natural Language Processing Techniques
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning

Volker Tresp has contributed extensively to scholarly literature, frequently publishing in several academic venues. The most common publication outlets for their work are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Lecture notes in computer science

Several recent papers highlight the range of their research focus. Notable works include:

  • TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs (2022), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs (2021), published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings (2020), published on arXiv (Cornell University)
  • A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models (2023), published on arXiv (Cornell University)
  • Classification by Attention: Scene Graph Classification with Prior Knowledge (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence

Throughout their career, Volker Tresp has collaborated frequently with several researchers. Their most common co-authors include:

  • Yunpu Ma
  • Jindong Gu
  • Sahand Sharifzadeh
  • Rajat Koner
  • Marcel Hildebrandt

Best Publications

  • A Three-Way Model for Collective Learning on Multi-Relational Data

    Maximilian Nickel;Volker Tresp;Hans-peter Kriegel

  • A Review of Relational Machine Learning for Knowledge Graphs

    Maximilian Nickel;Kevin Murphy;Volker Tresp;Evgeniy Gabrilovich

  • A Bayesian Committee Machine

    Volker Tresp

  • Learning Gaussian processes from multiple tasks

    Kai Yu;Volker Tresp;Anton Schwaighofer

  • Probabilistic memory-based collaborative filtering

    Kai Yu;A. Schwaighofer;V. Tresp;Xiaowei Xu

  • Factorizing YAGO: scalable machine learning for linked data

    Maximilian Nickel;Volker Tresp;Hans-Peter Kriegel

  • Active learning via transductive experimental design

    Kai Yu;Jinbo Bi;Volker Tresp

  • Method and device for the neuronal modelling of a dynamic system with non-linear stochastic behavior

    Thomas Briegel;Volker Tresp

  • Multi-label informed latent semantic indexing

    Kai Yu;Shipeng Yu;Volker Tresp

  • Representative sampling for text classification using support vector machines

    Zhao Xu;Kai Yu;Volker Tresp;Xiaowei Xu

  • Extraction of semantic biomedical relations from text using conditional random fields

    Markus Bundschus;Markus Bundschus;Mathaeus Dejori;Mathaeus Dejori;Martin Stetter;Volker Tresp

  • Stochastic Relational Models for Discriminative Link Prediction

    Kai Yu;Wei Chu;Shipeng Yu;Volker Tresp

  • Mixtures of Gaussian Processes

    Volker Tresp

  • Natural Language Questions for the Web of Data

    Mohamed Yahya;Klaus Berberich;Shady Elbassuoni;Maya Ramanath

  • Type-Constrained Representation Learning in Knowledge Graphs

    Denis Krompaβ;Stephan Baier;Volker Tresp

  • Learning Gaussian Process Kernels via Hierarchical Bayes

    Anton Schwaighofer;Volker Tresp;Kai Yu

  • Combining Estimators Using Non-Constant Weighting Functions

    Volker Tresp;Michiaki Taniguchi

  • Towards LarKC: A Platform for Web-Scale Reasoning

    D. Fensel;F. van Harmelen;B. Andersson;P. Brennan

  • Supervised probabilistic principal component analysis

    Shipeng Yu;Kai Yu;Volker Tresp;Hans-Peter Kriegel

  • Tensor-train recurrent neural networks for video classification

    Yinchong Yang;Denis Krompass;Volker Tresp

Frequent Co-Authors

Kai Yu
Kai Yu Horizon Robotics Inc.
Shipeng Yu
Shipeng Yu Pinterest
Hans-Peter Kriegel
Hans-Peter Kriegel Ludwig-Maximilians-Universität München
Hinrich Schütze
Hinrich Schütze Ludwig-Maximilians-Universität München
Thomas Seidl
Thomas Seidl Ludwig-Maximilians-Universität München
Kristian Kersting
Kristian Kersting Technical University of Darmstadt
Xiaowei Xu
Xiaowei Xu University of Arkansas at Little Rock
Stephan Günnemann
Stephan Günnemann Technical University of Munich
Evgeniy Gabrilovich
Evgeniy Gabrilovich Google (United States)

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