World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
16033
World Ranking
7756
National Ranking
462

Edward Grefenstette 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 Edward Grefenstette 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: 86 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Edward Grefenstette 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 Edward Grefenstette 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: 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

Edward Grefenstette is affiliated with University College London in the United Kingdom and primarily works in the field of Computer Science, focusing on Artificial Intelligence. Their published work spans several subfields including Computer Science Applications, Management Science and Operations Research, Computer Vision and Pattern Recognition, and General Health Professions.

Their main research topics highlight areas such as Reinforcement Learning in Robotics, Topic Modeling, Adversarial Robustness in Machine Learning, Natural Language Processing Techniques, Data Stream Mining Techniques, Explainable Artificial Intelligence (XAI), and Advanced Graph Neural Networks.

Frequent co-authors collaborating with Edward Grefenstette include:

  • Tim Rocktäschel
  • Minqi Jiang
  • Robert Kirk
  • Jack Parker-Holder
  • Heinrich Küttler

Their research has been published mainly in venues such as:

  • arXiv (Cornell University)
  • Journal of Artificial Intelligence Research
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Royal Society Open Science

Selected recent publications include:

  • "A Survey of Zero-shot Generalisation in Deep Reinforcement Learning" (2023, Journal of Artificial Intelligence Research)
  • "Differentiable Reasoning on Large Knowledge Bases and Natural Language" (2020, Proceedings of the AAAI Conference on Artificial Intelligence)
  • "Learning with AMIGo: Adversarially Motivated Intrinsic Goals" (2020, arXiv (Cornell University))
  • "The NetHack Learning Environment" (2020, arXiv (Cornell University))
  • "Learning Reasoning Strategies in End-to-End Differentiable Proving" (2020, arXiv (Cornell University))

Best Publications

  • A Convolutional Neural Network for Modelling Sentences

    Nal Kalchbrenner;Edward Grefenstette;Phil Blunsom

  • Teaching machines to read and comprehend

    Karl Moritz Hermann;Tomáš Kočiský;Edward Grefenstette;Lasse Espeholt

  • Hybrid computing using a neural network with dynamic external memory

    Alex Graves;Greg Wayne;Malcolm Reynolds;Tim Harley

  • Reasoning about Entailment with Neural Attention

    Tim Rocktäschel;Edward Grefenstette;Karl Moritz Hermann;Tomáš Ko iský;Tomáš Ko iský

  • Reasoning about Entailment with Neural Attention

    Tim Rocktäschel;Edward Grefenstette;Karl Moritz Hermann;Tomáš Kočiský

  • The NarrativeQA Reading Comprehension Challenge

    Tomáš Kočiský;Jonathan Schwarz;Phil Blunsom;Chris Dyer

  • Learning Explanatory Rules from Noisy Data

    Richard Evans;Edward Grefenstette

  • Latent Predictor Networks for Code Generation

    Wang Ling;Phil Blunsom;Edward Grefenstette;Karl Moritz Hermann

  • Experimental Support for a Categorical Compositional Distributional Model of Meaning

    Edward Grefenstette;Mehrnoosh Sadrzadeh

  • Learning to transduce with unbounded memory

    Edward Grefenstette;Karl Moritz Hermann;Mustafa Suleyman;Phil Blunsom

  • Discovering Discrete Latent Topics with Neural Variational Inference

    Yishu Miao;Edward Grefenstette;Phil Blunsom

  • A Survey of Reinforcement Learning Informed by Natural Language

    Jelena Luketina;Nantas Nardelli;Nantas Nardelli;Gregory Farquhar;Gregory Farquhar;Jakob N. Foerster

  • Latent Predictor Networks for Code Generation

    Wang Ling;Edward Grefenstette;Karl Moritz Hermann;Tomáš Kočiský

  • Analysing Mathematical Reasoning Abilities of Neural Models

    David Saxton;Edward Grefenstette;Felix Hill;Pushmeet Kohli

  • A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

    Unknown

  • Learning to Compose Words into Sentences with Reinforcement Learning

    Dani Yogatama;Phil Blunsom;Chris Dyer;Edward Grefenstette

  • Multi-Step Regression Learning for Compositional Distributional Semantics

    E. Grefenstette;G. Dinu;Y. Zhang;M. Sadrzadeh

  • Generalized Inner Loop Meta-Learning

    Edward Grefenstette;Brandon Amos;Denis Yarats;Phu Mon Htut

  • Learning to Understand Goal Specifications by Modelling Reward

    Dzmitry Bahdanau;Felix Hill;Jan Leike;Edward Hughes

  • Lambek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus

    Bob Coecke;Edward Grefenstette;Mehrnoosh Sadrzadeh

  • Can Neural Networks Understand Logical Entailment

    Richard Evans;David Saxton;David Amos;Pushmeet Kohli

  • The NetHack Learning Environment

    Heinrich Küttler;Nantas Nardelli;Alexander H. Miller;Roberta Raileanu

  • Learning Explanatory Rules from Noisy Data (Extended Abstract)

    Richard Evans;Edward Grefenstette

Frequent Co-Authors

Phil Blunsom
Phil Blunsom University of Oxford
Tim Rocktäschel
Tim Rocktäschel University College London
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Felix Hill
Felix Hill Google (United States)
Chris Dyer
Chris Dyer Google (United States)
Sebastian Riedel
Sebastian Riedel University College London
Jakob Foerster
Jakob Foerster University of Oxford
Stephen Pulman
Stephen Pulman University of Oxford
Stephen Clark
Stephen Clark Cambridge Quantum Computing
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)

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