World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
24071
World Ranking
2276
National Ranking
130

Sebastian Riedel 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 Sebastian Riedel 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: 204 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Sebastian Riedel 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 Sebastian Riedel 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: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Overview

Sebastian Riedel is affiliated with University College London in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Control and Systems Engineering.

The main topics covered in their work include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Data Quality and Management
  • Advanced Graph Neural Networks
  • Robot Manipulation and Learning

Sebastian Riedel has published extensively, with a significant number of papers appearing in the following venues:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Findings of the Association for Computational Linguistics: NAACL 2022

Recent publications by Riedel include:

  • Contextual Personal Intelligence: A New Paradigm for AI That Evolves With You (2025, arXiv)
  • Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Long Papers)
  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024, arXiv)
  • Autoregressive Entity Retrieval (2020, arXiv)
  • Atlas: Few-shot Learning with Retrieval Augmented Language Models (2022, arXiv)

Sebastian Riedel has collaborated frequently with several researchers, including:

  • Pontus Stenetorp
  • Fabio Petroni
  • Patrick Lewis
  • Pasquale Minervini
  • Gautier Izacard

Best Publications

  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Patrick S. H. Lewis;Ethan Perez;Aleksandra Piktus;Fabio Petroni

  • Language Models as Knowledge Bases

    Fabio Petroni;Tim Rocktäschel;Patrick S. H. Lewis;Anton Bakhtin

  • Convolutional 2D Knowledge Graph Embeddings

    Tim Dettmers;Pasquale Minervini;Pontus Stenetorp;Sebastian Riedel

  • Complex embeddings for simple link prediction

    Théo Trouillon;Johannes Welbl;Sebastian Riedel;Éric Gaussier

  • Modeling relations and their mentions without labeled text

    Sebastian Riedel;Limin Yao;Andrew McCallum

  • Convolutional 2D knowledge graph embeddings

    Tim Dettmers;Pasquale Minervini;Pontus Stenetorp;Sebastian Riedel

  • The CoNLL 2007 Shared Task on Dependency Parsing

    Joakim Nivre;Johan Hall;Sandra K"ubler;Ryan McDonald

  • Relation Extraction with Matrix Factorization and Universal Schemas

    Sebastian Riedel;Limin Yao;Andrew McCallum;Benjamin M. Marlin

  • Constructing Datasets for Multi-hop Reading Comprehension Across Documents

    Johannes Welbl;Pontus Stenetorp;Sebastian Riedel

  • Fact Checking: Task definition and dataset construction

    Andreas Vlachos;Sebastian Riedel

  • TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

    Pengcheng Yin;Graham Neubig;Wen-tau Yih;Sebastian Riedel

  • Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

    Yao Lu;Max Bartolo;Alastair Moore;Sebastian Riedel

  • MLQA: Evaluating Cross-lingual Extractive Question Answering

    Patrick S. H. Lewis;Barlas Oguz;Ruty Rinott;Sebastian Riedel

  • Scalable Zero-shot Entity Linking with Dense Entity Retrieval

    Ledell Wu;Fabio Petroni;Martin Josifoski;Sebastian Riedel

  • SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

    Isabelle Augenstein;Mrinal Das;Sebastian Riedel;Lakshmi Vikraman

  • emoji2vec: Learning Emoji Representations from their Description

    Ben Eisner;Tim Rocktäschel;Isabelle Augenstein;Matko Bosnjak

  • Injecting Logical Background Knowledge into Embeddings for Relation Extraction

    Tim Rocktäschel;Sameer Singh;Sebastian Riedel

  • KILT: a Benchmark for Knowledge Intensive Language Tasks

    Fabio Petroni;Aleksandra Piktus;Angela Fan;Patrick S. H. Lewis

  • A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

    Benjamin Riedel;Isabelle Augenstein;Georgios P. Spithourakis;Sebastian Riedel

  • Knowledge graph completion via complex tensor factorization

    Théo Trouillon;Christopher R. Dance;Éric Gaussier;Johannes Welbl

  • Dynabench: Rethinking Benchmarking in NLP.

    Douwe Kiela;Max Bartolo;Yixin Nie;Divyansh Kaushik

  • Knowledge Graph Completion via Complex Tensor Factorization

    Théo Trouillon;Christopher R. Dance;Johannes Welbl;Sebastian Riedel

Frequent Co-Authors

Tim Rocktäschel
Tim Rocktäschel University College London
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Sameer Singh
Sameer Singh University of California, Irvine
Isabelle Augenstein
Isabelle Augenstein University of Copenhagen
Wen-tau Yih
Wen-tau Yih Facebook (United States)
Andreas Vlachos
Andreas Vlachos University of Cambridge
Fabrizio Silvestri
Fabrizio Silvestri Sapienza University of Rome
Douwe Kiela
Douwe Kiela Stanford University
Edward Grefenstette
Edward Grefenstette University College London
Yuji Matsumoto
Yuji Matsumoto Nara Institute of Science and Technology

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