World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
17916
World Ranking
3758
National Ranking
225

Stephen Muggleton 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 Stephen Muggleton 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: 252 publications — 63rd percentile

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

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

Stephen Muggleton 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 Stephen Muggleton 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2010 - Fellow of the Royal Academy of Engineering (UK)
  • 2002 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and practice of inductive logic programming, especially applied to the discovery of new biomolecular theories from observational data.

Overview

Stephen Muggleton is affiliated with Imperial College London in the United Kingdom. Their research spans multiple disciplines, primarily within computer science and biochemistry, genetics, and molecular biology.

The main fields of study in Stephen Muggleton's work include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

The scientist's research covers detailed subfields such as:

  • Artificial Intelligence
  • Molecular Biology
  • Computational Theory and Mathematics
  • Information Systems
  • Biophysics

Main topics addressed in their work include:

  • Logic, Reasoning, and Knowledge
  • Explainable Artificial Intelligence (XAI)
  • Topic Modeling
  • Gene Regulatory Network Analysis
  • Computability, Logic, AI Algorithms
  • AI-based Problem Solving and Planning
  • Machine Learning and Algorithms

Stephen Muggleton has published extensively, with frequent appearances in several venues. The top publication venues are:

  • arXiv (Cornell University)
  • Machine Learning
  • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  • Proceedings of the AAAI Conference on Artificial Intelligence

Recent notable papers include:

  • Inductive logic programming at 30, 2021, published in Machine Learning
  • Beneficial and harmful explanatory machine learning, 2021, published in Machine Learning
  • Introduction to 'Cognitive artificial intelligence', 2023, published in Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  • Explanatory machine learning for sequential human teaching, 2023, published in Machine Learning
  • Top program construction and reduction for polynomial time Meta-Interpretive learning, 2021, published in Machine Learning

Frequent co-authors collaborating with Stephen Muggleton include:

  • Lun Ai
  • Geoff Baldwin
  • Shi-Shun Liang
  • Ute Schmid
  • Stassa Patsantzis

Stephen Muggleton has received professional recognition, including the following awards:

  • Fellow of the Royal Academy of Engineering (UK), awarded in 2010
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), awarded in 2002, for contributions to the theory and practice of inductive logic programming, especially in biomolecular theory discovery from observational data

Best Publications

  • Inductive Logic Programming : Theory and Methods

    Stephen Muggleton;Luc de Raedt

  • Inverse entailment and PROGOL

    Stephen Muggleton

  • Efficient Induction of Logic Programs

    S. Muggleton;C. Feng

  • Functional genomic hypothesis generation and experimentation by a robot scientist

    Ross D. King;Kenneth E. Whelan;Ffion M. Jones;Philip G. K. Reiser

  • Machine invention of first order predicates by inverting resolution

    Stephen Muggleton;Wray L. Buntine

  • Theories for mutagenicity: a study in first-order and feature-based induction

    Ashwin Srinivasan;S. H. Muggleton;M. J. E. Sternberg;R. D. King

  • Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.

    Ross D. King;Stephen Muggleton;Richard A. Lewis;Michael J. E. Sternberg

  • Protein secondary structure prediction using logic-based machine learning

    S. Muggleton;R.D. King;M.J.E. Sternberg

  • Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

    Ross D. King;Stephen H. Muggleton;Ashwin Srinivasan;Michael J. E. Sternberg

  • Stochastic Logic Programs

    Unknown

  • Learning from Positive Data

    Stephen Muggleton

  • Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

    Stephen H. Muggleton;Dianhuan Lin;Alireza Tamaddoni-Nezhad

  • Applications of inductive logic programming

    Ivan Bratko;Stephen Muggleton

  • Duce, an oracle-based approach to constructive induction

    Stephen Muggleton

  • Inductive programming meets the real world

    Sumit Gulwani;José Hernández-Orallo;Emanuel Kitzelmann;Stephen H. Muggleton

  • Carcinogenesis Predictions Using ILP

    Ashwin Srinivasan;Ross D. King;Stephen Muggleton;Michael J. E. Sternberg

  • ILP turns 20

    Stephen Muggleton;Luc Raedt;David Poole;Ivan Bratko

  • Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL

    Paul Finn;Stephen Muggleton;David Page;Ashwin Srinivasan

  • Meta-interpretive learning: application to grammatical inference

    Stephen H. Muggleton;Dianhuan Lin;Niels Pahlavi;Alireza Tamaddoni-Nezhad

  • The predictive toxicology evaluation challenge

    A. Srinivasan;R. D. King;S. H. Muggleton;M. J. E. Sternberg

  • Learning Stochastic Logic Programs

    Stephen Muggleton

  • Inductive logic programming

    Stephen Muggleton

Frequent Co-Authors

Michael J.E. Sternberg
Michael J.E. Sternberg Imperial College London
Ross D. King
Ross D. King University of Manchester
Luc De Raedt
Luc De Raedt KU Leuven
David A. Bohan
David A. Bohan INRAE : Institut national de recherche pour l'agriculture, l'alimentation et l'environnement
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Antonis C. Kakas
Antonis C. Kakas University of Cyprus
Lise Getoor
Lise Getoor University of California, Santa Cruz
Ivan Bratko
Ivan Bratko University of Ljubljana
Stephen G. Oliver
Stephen G. Oliver University of Cambridge

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens doors to a wide range of online learning and career options. For those seeking business and technology skills, a cheap mba online can offer valuable management experience affordably. This can be a smart choice if you aim for leadership roles or want to broaden your business expertise.

If you prefer a quick and focused upskilling path, look into online masters degrees. Many programs can be completed in a year and cover specialized topics ideal for career advancement.

Additionally, there are quick degrees that pay well, providing a fast-track to high-demand roles in tech and related fields. These programs are perfect if you want to enter the workforce quickly with competitive salaries.

For those interested in the future of technology, consider online degrees in ai. Artificial intelligence skills are increasingly sought after in both industry and research. Exploring these pathways can enhance your expertise and prepare you for evolving tech careers.

Best Scientists Citing Stephen Muggleton

Trending Scientists

Recently Published Articles