World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
12358
World Ranking
4784
National Ranking
284

Murray Shanahan 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 Murray Shanahan 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.

Murray Shanahan 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 Murray Shanahan 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: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Overview

Murray Shanahan is affiliated with Imperial College London in the United Kingdom. Their research focuses primarily within the field of Computer Science, with 69 publications. Within this broad field, Shanahan has contributed extensively to subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Safety Research, and Social Psychology.

The scientist's work covers a range of topics including Topic Modeling, Natural Language Processing Techniques, Explainable Artificial Intelligence (XAI), Reinforcement Learning in Robotics, Domain Adaptation and Few-Shot Learning, Ethics and Social Impacts of AI, and Neural dynamics and brain function.

Shanahan's recent publications include:

  • "Role play with large language models" (2023, Nature)
  • "Talking about Large Language Models" (2024, Communications of the ACM)
  • "Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning" (2022, arXiv (Cornell University))
  • "Rethink reporting of evaluation results in AI" (2023, Science)
  • "Talking About Large Language Models" (2022, arXiv (Cornell University))

The main publication venues for Shanahan include arXiv (Cornell University) with 33 papers, Trends in Cognitive Sciences with 2 publications, Nature, Communications of the ACM, and Science with one publication each.

Frequent collaborators in Shanahan's research include:

  • Antonia Creswell
  • Lucy G. Cheke
  • Kyriacos Nikiforou
  • Borja G. León
  • Francesco Belardinelli

Best Publications

  • The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs

    Robin Lester Carhart-Harris;Robert Leech;Peter John Hellyer;Murray Shanahan

  • The event calculus explained

    Murray Shanahan

  • Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

    Nat Dilokthanakul;Pedro A. M. Mediano;Marta Garnelo;Matthew C. H. Lee

  • Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia

    Murray Shanahan

  • Role play with large language models

    Unknown

  • Prediction is deduction but explanation is abduction

    Murray Shanahan

  • A cognitive architecture that combines internal simulation with a global workspace

    Murray Shanahan

  • Metastable chimera states in community-structured oscillator networks.

    Murray Shanahan

  • Conditional Neural Processes

    Marta Garnelo;Dan Rosenbaum;Christopher Maddison;Tiago Ramalho

  • Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning

    Unknown

  • Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

    Marta Garnelo;Murray Shanahan

  • Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis.

    Murray Shanahan;Verner P. Bingman;Toru Shimizu;Martin Wild

  • Applying Global Workspace Theory to the Frame Problem.

    Murray Shanahan;Bernard J. Baars

  • Accelerated simulation of spiking neural networks using GPUs

    Andreas K. Fidjeland;Murray P. Shanahan

  • The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention.

    Peter J. Hellyer;Murray Shanahan;Gregory Scott;Richard J. S. Wise

  • Relational Deep Reinforcement Learning.

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity

    Alexandros Bouganis;Murray Shanahan

  • An abductive event calculus planner

    Murray Shanahan

  • Towards Deep Symbolic Reinforcement Learning

    Marta Garnelo;Kai Arulkumaran;Murray Shanahan

  • Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome

    Peter J. Hellyer;Gregory Scott;Murray Shanahan;David J. Sharp

  • Perception as abduction: turning sensor data into meaningful representation.

    Murray Shanahan

  • The Event Calculus in Classical Logic - Alternative Axiomatisations.

    Rob Miller;Murray Shanahan

  • Deep reinforcement learning with relational inductive biases

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

Frequent Co-Authors

Claudia Clopath
Claudia Clopath Imperial College London
Anil K. Seth
Anil K. Seth University of Sussex
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
Demis Hassabis
Demis Hassabis Google (United States)
Robert Leech
Robert Leech King's College London
Matthew Botvinick
Matthew Botvinick Yale University
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Onur Güntürkün
Onur Güntürkün Ruhr University Bochum
David J. Sharp
David J. Sharp Imperial College London

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