World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
5999
World Ranking
11197
National Ranking
202

Joaquin Vanschoren 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 Joaquin Vanschoren 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: 154 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Joaquin Vanschoren 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 Joaquin Vanschoren 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

Joaquin Vanschoren is affiliated with Eindhoven University of Technology in the Netherlands. Their primary field of study is computer science, with a focus on artificial intelligence and related subfields.

Their research activity spans various subfields including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Radiology, Nuclear Medicine and Imaging
  • Information Systems and Management

Joaquin Vanschoren's work covers multiple topics, particularly within machine learning and data analysis:

  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Algorithms
  • Data Stream Mining Techniques
  • Anomaly Detection Techniques and Applications
  • Imbalanced Data Classification Techniques
  • COVID-19 diagnosis using AI

The scientist has published extensively in several venues, with a notable frequency in the following publication sources:

  • arXiv (Cornell University)
  • Machine Learning
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Sensors
  • Proceedings of the Genetic and Evolutionary Computation Conference Companion

Some of their recent papers include:

  • Advances and Challenges in Meta-Learning: A Technical Review, 2024, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Importance of Tuning Hyperparameters of Machine Learning Algorithms, 2020, arXiv (Cornell University)
  • Meta-features for meta-learning, 2022, Knowledge-Based Systems
  • Adaptation Strategies for Automated Machine Learning on Evolving Data, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • DataPerf: Benchmarks for Data-Centric AI Development, 2022, arXiv (Cornell University)

Their frequent co-authors include:

  • Jan N. van Rijn
  • Carlos Soares
  • Pavel Brazdil
  • Murat Onur Yildirim
  • Elif Ceren Gok Yildirim

Joaquin Vanschoren is also an author of books published by Springer Nature, notably a book titled Metalearning published in 2022.

Best Publications

  • OpenML: networked science in machine learning

    Joaquin Vanschoren;Jan N. van Rijn;Bernd Bischl;Luis Torgo

  • Automated Machine Learning

    Frank Hutter;Lars Kotthoff;Joaquin Vanschoren

  • Meta-Learning: A Survey

    Joaquin Vanschoren

  • ASlib: A Benchmark Library for Algorithm Selection

    Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer

  • Advances and Challenges in Meta-Learning: A Technical Review

    Unknown

  • An Open Source AutoML Benchmark

    Pieter Gijsbers;Erin LeDell;Janek Thomas;Sébastien Poirier

  • OpenML: A collaborative science platform

    Jan van Rijn;Bernd Bischl;Luis Torgo;Bo Gao

  • Effectiveness of Random Search in SVM hyper-parameter tuning

    Rafael G. Mantovani;Andre L. D. Rossi;Joaquin Vanschoren;Bernd Bischl

  • A survey of intelligent assistants for data analysis

    Floarea Serban;Joaquin Vanschoren;Jörg-Uwe Kietz;Abraham Bernstein

  • The online performance estimation framework: heterogeneous ensemble learning for data streams

    Jan N. van Rijn;Jan N. van Rijn;Geoffrey Holmes;Bernhard Pfahringer;Joaquin Vanschoren

  • Experiment databases

    Joaquin Vanschoren;Hendrik Blockeel;Bernhard Pfahringer;Geoffrey Holmes

  • Selecting classification algorithms with active testing

    Rui Leite;Pavel Brazdil;Joaquin Vanschoren

  • Meta-features for meta-learning

    Unknown

  • Hyper-Parameter Tuning of a Decision Tree Induction Algorithm

    Rafael G. Mantovani;Tomas Horvath;Ricardo Cerri;Joaquin Vanschoren

  • Importance of Tuning Hyperparameters of Machine Learning Algorithms.

    Hilde J. P. Weerts;Andreas C. Mueller;Joaquin Vanschoren

  • Fast Algorithm Selection Using Learning Curves

    Jan N. van Rijn;Salisu Mamman Abdulrahman;Pavel Brazdil;Joaquin Vanschoren

  • DataPerf: Benchmarks for Data-Centric AI Development

    Unknown

  • Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning

    Hendrik Blockeel;Joaquin Vanschoren

  • Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

    Iván Olier;Iván Olier;Noureddin Sadawi;Noureddin Sadawi;G. Richard J. Bickerton;Joaquin Vanschoren

  • Adaptation Strategies for Automated Machine Learning on Evolving Data

    Bilge Celik;Joaquin Vanschoren

  • Algorithm selection on data streams

    Jan N. van Rijn;Geoffrey Holmes;Bernhard Pfahringer;Joaquin Vanschoren

  • Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants

    Yezi Zhu;Marc Aoun;Marcel P.C.M. Krijn;J. Vanschoren

  • OpenML Benchmarking Suites and the OpenML100.

    Bernd Bischl;Giuseppe Casalicchio;Matthias Feurer;Frank Hutter

  • OpenML-Python: an extensible Python API for OpenML

    Matthias Feurer;Jan N. van Rijn;Jan N. van Rijn;Arlind Kadra;Pieter Gijsbers

Frequent Co-Authors

Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München
Frank Hutter
Frank Hutter University of Freiburg
Geoffrey Holmes
Geoffrey Holmes University of Waikato
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
André C. P. L. F. de Carvalho
André C. P. L. F. de Carvalho Universidade de São Paulo
Ross D. King
Ross D. King University of Manchester
Carlos Soares
Carlos Soares University of Porto
Crina Grosan
Crina Grosan King's College London
Daniel Lakens
Daniel Lakens Eindhoven University of Technology

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