World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
19413
World Ranking
5481
National Ranking
113

Rico Sennrich 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 Rico Sennrich 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: 168 publications — 34th percentile

34% of scientists in this discipline score the same or lower.

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

Rico Sennrich 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 Rico Sennrich 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Rico Sennrich is affiliated with the University of Zurich in Switzerland. Their research primarily focuses on computer science and its subfields, with a strong emphasis on artificial intelligence, including applications in computer vision and pattern recognition, human-computer interaction, developmental and educational psychology, and language and linguistics.

Their work covers several main topics, including natural language processing techniques, topic modeling, multimodal machine learning applications, text readability and simplification, speech recognition and synthesis, explainable artificial intelligence (XAI), and sentiment analysis and opinion mining.

Recent papers authored or co-authored by Rico Sennrich include:

  • Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation, 2020, arXiv (Cornell University)
  • A Set of Recommendations for Assessing Human-Machine Parity in Language Translation, 2020, Zurich Open Repository and Archive (University of Zurich)
  • Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation, 2021, Zurich Open Repository and Archive (University of Zurich)
  • X-Stance: A Multilingual Multi-Target Dataset for Stance Detection, 2020, arXiv (Cornell University)
  • Domain robustness in neural machine translation, 2020, Zurich Open Repository and Archive (University of Zurich)

Sennrich frequently collaborates with several co-authors, including Jannis Vamvas, Ivan Titov, Biao Zhang, Chantal Amrhein, and Annette Rios.

The primary venues where their work has been published include:

  • arXiv (Cornell University)
  • Zurich Open Repository and Archive (University of Zurich)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Best Publications

  • Neural Machine Translation of Rare Words with Subword Units

    Rico Sennrich;Barry Haddow;Alexandra Birch

  • Improving Neural Machine Translation Models with Monolingual Data

    Rico Sennrich;Barry Haddow;Alexandra Birch

  • Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

    Elena Voita;Elena Voita;David Talbot;Fedor Moiseev;Fedor Moiseev;Rico Sennrich

  • Edinburgh Neural Machine Translation Systems for WMT 16

    Rico Sennrich;Barry Haddow;Alexandra Birch

  • Linguistic Input Features Improve Neural Machine Translation

    Rico Sennrich;Barry Haddow

  • Nematus: a Toolkit for Neural Machine Translation

    Rico Sennrich;Orhan Firat;Kyunghyun Cho;Alexandra Birch

  • Context-Aware Neural Machine Translation Learns Anaphora Resolution

    Elena Voita;Elena Voita;Pavel Serdyukov;Rico Sennrich;Rico Sennrich;Ivan Titov;Ivan Titov

  • Controlling Politeness in Neural Machine Translation via Side Constraints

    Rico Sennrich;Barry Haddow;Alexandra Birch

  • Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures

    Gongbo Tang;Matthias Muller;Annette Rios;Rico Sennrich

  • Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation

    Samuel Läubli;Rico Sennrich;Martin Volk

  • Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation

    Biao Zhang;Philip Williams;Ivan Titov;Rico Sennrich

  • Paraphrasing Revisited with Neural Machine Translation

    Jonathan Mallinson;Rico Sennrich;Mirella Lapata

  • Revisiting Low-Resource Neural Machine Translation: A Case Study

    Rico Sennrich;Biao Zhang

  • Evaluating Discourse Phenomena in Neural Machine Translation

    Rachel Bawden;Rico Sennrich;Alexandra Birch;Barry Haddow

  • When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion

    Elena Voita;Elena Voita;Rico Sennrich;Ivan Titov

  • The University of Edinburgh's Neural MT Systems for WMT17

    Rico Sennrich;Alexandra Birch;Anna Currey;Ulrich Germann

  • Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation

    Rico Sennrich

  • The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives

    Elena Voita;Elena Voita;Rico Sennrich;Ivan Titov

  • How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs

    Rico Sennrich

  • Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

    Rico Sennrich

  • Synthesis Lectures on Human Language Technologies

    Philip Williams;Rico Sennrich;Matt Post;Philipp Koehn

Frequent Co-Authors

Barry Haddow
Barry Haddow University of Edinburgh
Ivan Titov
Ivan Titov University of Edinburgh
Alexandra Birch
Alexandra Birch University of Edinburgh
Philipp Koehn
Philipp Koehn Johns Hopkins University
Joakim Nivre
Joakim Nivre Uppsala University
Andy Way
Andy Way Dublin City University
Alex Waibel
Alex Waibel Carnegie Mellon University
Mirella Lapata
Mirella Lapata University of Edinburgh
Ondrej Bojar
Ondrej Bojar Charles University
Siva Reddy
Siva Reddy McGill University

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