World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
28266
World Ranking
12828
National Ranking
5168

Veselin Stoyanov 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 Veselin Stoyanov 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: 64 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Veselin Stoyanov 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 Veselin Stoyanov 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Veselin Stoyanov is affiliated with Facebook in the United States and has contributed extensively to the field of computer science with a particular focus on artificial intelligence. Their research spans multiple domains including topic modeling, multimodal machine learning applications, and natural language processing techniques.

Stoyanov's publication record shows a significant emphasis on addressing challenges related to domain adaptation and few-shot learning, as well as speech recognition and synthesis. Their contributions also touch on interpreting and communication in healthcare and the detection of hate speech and cyberbullying.

The scientist's recent papers include the following:

  • Towards Learning Terminological Concept Systems from Multilingual Natural Language Text, 2021, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Few-shot Learning with Multilingual Language Models, 2021, arXiv (Cornell University)
  • Prompt-free and Efficient Few-shot Learning with Language Models, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Improving In-Context Few-Shot Learning via Self-Supervised Training, 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs, 2021, arXiv (Cornell University)

Frequent coauthors involved in collaborations with Stoyanov are:

  • Jingfei Du
  • Luke Zettlemoyer
  • Ramakanth Pasunuru
  • Zornitsa Kozareva
  • Myle Ott

Stoyanov's work has appeared primarily in the following publication venues:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The main fields of study where Stoyanov has contributed include computer science, with an emphasis on artificial intelligence and computer vision and pattern recognition. Their multidisciplinary approach integrates various technical areas to address complex computational problems involving language and multimodal data processing.

Best Publications

  • RoBERTa: A Robustly Optimized BERT Pretraining Approach

    Yinhan Liu;Myle Ott;Naman Goyal;Jingfei Du

  • BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

    Mike Lewis;Yinhan Liu;Naman Goyal;Marjan Ghazvininejad

  • Unsupervised Cross-lingual Representation Learning at Scale

    Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary

  • SemEval-2016 Task 4: Sentiment Analysis in Twitter

    Preslav Nakov;Alan Ritter;Sara Rosenthal;Fabrizio Sebastiani

  • XNLI: Evaluating Cross-lingual Sentence Representations

    Alexis Conneau;Ruty Rinott;Guillaume Lample;Adina Williams

  • Unsupervised Cross-lingual Representation Learning at Scale.

    Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary

  • SemEval-2013 Task 2: Sentiment Analysis in Twitter

    Preslav Nakov;Sara Rosenthal;Zornitsa Kozareva;Veselin Stoyanov

  • SemEval-2014 Task 9: Sentiment Analysis in Twitter

    Sara Rosenthal;Alan Ritter;Preslav Nakov;Veselin Stoyanov

  • SemEval-2015 Task 10: Sentiment Analysis in Twitter

    Sara Rosenthal;Preslav Nakov;Svetlana Kiritchenko;Saif Mohammad

  • System and method for automatically summarizing fine-grained opinions in digital text

    Claire Cardie;Veselin Stoyanov;Yejin Choi;Eric Breck

  • XNLI: Evaluating Cross-lingual Sentence Representations

    Alexis Conneau;Guillaume Lample;Ruty Rinott;Adina Williams

  • Emerging Cross-lingual Structure in Pretrained Language Models

    Alexis Conneau;Shijie Wu;Haoran Li;Luke Zettlemoyer

  • Few-shot Learning with Multilingual Generative Language Models

    Unknown

  • Conundrums in Noun Phrase Coreference Resolution: Making Sense of the State-of-the-Art

    Veselin Stoyanov;Nathan Gilbert;Claire Cardie;Ellen Riloff

  • Topic Identification for Fine-Grained Opinion Analysis

    Veselin Stoyanov;Claire Cardie

  • Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model

    Wenhan Xiong;Jingfei Du;William Yang Wang;Veselin Stoyanov

  • Multi-Perspective Question Answering Using the OpQA Corpus

    Veselin Stoyanov;Claire Cardie;Janyce Wiebe

  • Efficient Large Scale Language Modeling with Mixtures of Experts

    Unknown

  • Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art.

    Patrick S. H. Lewis;Myle Ott;Jingfei Du;Veselin Stoyanov

  • Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure

    Veselin Stoyanov;Alexander Ropson;Jason Eisner

  • Self-training Improves Pre-training for Natural Language Understanding

    Jingfei Du;Edouard Grave;Beliz Gunel;Vishrav Chaudhary

  • Coreference Resolution with Reconcile

    Veselin Stoyanov;Claire Cardie;Nathan Gilbert;Ellen Riloff

  • A multi-lingual multi-task architecture for low-resource sequence labeling

    Ying Lin;Shengqi Yang;Veselin Stoyanov;Heng Ji

  • Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

    Preslav Nakov;Sara Rosenthal;Svetlana Kiritchenko;Saif M. Mohammad

  • Emerging Cross-lingual Structure in Pretrained Language Models

    Shijie Wu;Alexis Conneau;Haoran Li;Luke Zettlemoyer

  • SemEval-2015 Task 10: Sentiment Analysis in Twitter

    Sara Rosenthal;Saif M Mohammad;Preslav Nakov;Alan Ritter

  • SemEval-2013 Task 2: Sentiment Analysis in Twitter

    Preslav Nakov;Zornitsa Kozareva;Alan Ritter;Sara Rosenthal

  • SemEval-2014 Task 9: Sentiment Analysis in Twitter

    Sara Rosenthal;Preslav Nakov;Alan Ritter;Veselin Stoyanov

Frequent Co-Authors

Claire Cardie
Claire Cardie Cornell University
Alexis Conneau
Alexis Conneau Facebook (United States)
Preslav Nakov
Preslav Nakov Mohamed bin Zayed University of Artificial Intelligence
Alan Ritter
Alan Ritter Georgia Institute of Technology
Luke Zettlemoyer
Luke Zettlemoyer University of Washington
Myle Ott
Myle Ott Facebook (United States)
Michael Lewis
Michael Lewis University of Pittsburgh
Edouard Grave
Edouard Grave Facebook (United States)
Ellen Riloff
Ellen Riloff University of Utah
Janyce Wiebe
Janyce Wiebe University of Pittsburgh

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