World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
12275
World Ranking
7793
National Ranking
3372

Mark Craven 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 Mark Craven 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: 110 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Mark Craven 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 Mark Craven 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Mark Craven is affiliated with the University of Wisconsin-Madison in the United States and has contributed to research primarily within the fields of Biochemistry, Genetics and Molecular Biology, and Medicine. Their work spans a variety of interdisciplinary subfields including Molecular Biology, Genetics, Artificial Intelligence, Biomedical Engineering, and Physiology.

Craven's research addresses topics such as genetic and phenotypic traits in livestock, asthma and respiratory diseases, IL-33, ST2, and ILC pathways, explainable artificial intelligence (XAI), machine learning and data classification, SARS-CoV-2 detection and testing, as well as biosensors and analytical detection.

Several recent papers illustrate the scope of their scholarly contributions:

  • Defining mitochondrial protein functions through deep multiomic profiling (2022, Nature)
  • Expression quantitative trait locus fine mapping of the 17q12-21 asthma locus in African American children: a genetic association and gene expression study (2020, The Lancet Respiratory Medicine)
  • Deciphering the impact of genomic variation on function (2024, Nature)
  • Machine learning for syndromic surveillance using veterinary necropsy reports (2020, PLoS ONE)
  • New Insights Relating Gasdermin B to the Onset of Childhood Asthma (2022, American Journal of Respiratory Cell and Molecular Biology)

Frequent collaborators of Mark Craven include Leonard B. Bacharier, Tiago Luciano Passafaro, Fernando Brito Lopes, J.R.R. Dórea, and Vivian Breen, each having coauthored multiple publications with Craven.

Mark Craven's research has been published in journals with repeated appearances in Nature and Research Square, as well as in The Lancet Respiratory Medicine, PLoS ONE, and the American Journal of Respiratory Cell and Molecular Biology.

Best Publications

  • An Analysis of Active Learning Strategies for Sequence Labeling Tasks

    Burr Settles;Mark Craven

  • Learning to extract symbolic knowledge from the World Wide Web

    Mark Craven;Dan DiPasquo;Dayne Freitag;Andrew McCallum

  • Extracting Tree-Structured Representations of Trained Networks

    Mark Craven;Jude W. Shavlik

  • Constructing Biological Knowledge Bases by Extracting Information from Text Sources

    Mark Craven;Johan Kumlien

  • Learning to construct knowledge bases from the World Wide Web

    Mark Craven;Dan DiPasquo;Dayne Freitag;Andrew McCallum

  • Multiple-Instance Active Learning

    Burr Settles;Mark Craven;Soumya Ray

  • Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

    David Andrzejewski;Xiaojin Zhu;Mark Craven

  • Using sampling and queries to extract rules from trained neural networks

    Mark Craven;Jude W. Shavlik

  • Using neural networks for data mining

    Mark W. Craven;Jude W. Shavlik

  • Extracting comprehensible models from trained neural networks

    Mark William Craven;Jude W. Shavlik

  • Supervised versus multiple instance learning: an empirical comparison

    Soumya Ray;Mark Craven

  • Identification of toxicologically predictive gene sets using cDNA microarrays

    Russell S. Thomas;David R. Rank;Sharron G. Penn;Gina M. Zastrow

  • Active Learning with Real Annotation Costs

    Burr Settles;Mark Craven;Lewis Friedland

  • Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

    Tina Eliassi-Rad;Lyle Ungar;Mark Craven;Dimitrios Gunopulos

  • Hierarchical hidden Markov models for information extraction

    Marios Skounakis;Mark Craven;Soumya Ray

  • Representing sentence structure in hidden Markov models for information extraction

    Soumya Ray;Mark Craven

  • Relational learning with statistical predicate invention: better models for hypertext

    Mark Craven;Seán Slattery

  • A framework for incorporating general domain knowledge into latent Dirichlet allocation using first-order logic

    David Andrzejewski;Xiaojin Zhu;Mark Craven;Benjamin Recht

  • Learning symbolic rules using artificial neural networks

    Mark Craven;Jude W. Shavlik

  • A Bayesian network approach to operon prediction.

    Joseph Bockhorst;Mark W. Craven;David Page;Jude W. Shavlik

  • Combining Statistical and Relational Methods for Learning in Hypertext Domains

    Seán Slattery;Mark Craven

Frequent Co-Authors

Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Christopher A. Bradfield
Christopher A. Bradfield University of Wisconsin–Madison
Paul Ahlquist
Paul Ahlquist University of Wisconsin–Madison
Audrey P. Gasch
Audrey P. Gasch University of Wisconsin–Madison
Xiaojin Zhu
Xiaojin Zhu University of Wisconsin–Madison
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Andreas Vlachos
Andreas Vlachos University of Cambridge
Diane R. Gold
Diane R. Gold Harvard University
Carole Ober
Carole Ober University of Chicago

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Related Online Degrees & Career Pathways

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For students considering engineering, the online electrical engineering career outcomes range from communications to embedded systems—skills that complement many areas of computer science. Exploring these related online degrees expands your future opportunities and helps you make informed choices about your educational path.

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