World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
10760
World Ranking
12856
National Ranking
5183

Daniel Cer 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 Daniel Cer 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: 74 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Daniel Cer 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 Daniel Cer 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

Daniel Cer is a researcher affiliated with Google in the United States, specializing in computer science with a primary focus on artificial intelligence. Over their career, they have produced 58 publications, mainly centered around artificial intelligence, with additional contributions to computer vision and pattern recognition, information systems, and health informatics.

Their research spans multiple main topics including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Domain Adaptation and Few-Shot Learning
  • Sentiment Analysis and Opinion Mining
  • Hate Speech and Cyberbullying Detection

Daniel Cer frequently publishes in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Findings of the Association for Computational Linguistics: ACL 2022

Their recent notable papers include:

  • "Language-agnostic BERT Sentence Embedding" (2022), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models" (2022), Findings of the Association for Computational Linguistics: ACL 2022
  • "Gemma: Open Models Based on Gemini Research and Technology" (2024), arXiv (Cornell University)
  • "Language-agnostic BERT Sentence Embedding" (2020), arXiv (Cornell University)
  • "SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer" (2022), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Regular collaborators of Daniel Cer include:

  • Yinfei Yang
  • Gustavo Hernández Ábrego
  • Noah Constant
  • Jianmo Ni
  • Tu Vu

Best Publications

  • Universal Sentence Encoder

    Daniel Cer;Yinfei Yang;Sheng-yi Kong;Nan Hua

  • SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

    Daniel M. Cer;Mona T. Diab;Eneko Agirre;Iñigo Lopez-Gazpio

  • Universal Sentence Encoder for English

    Daniel Cer;Yinfei Yang;Sheng-yi Kong;Nan Hua

  • SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

    Daniel Cer;Mona Diab;Eneko Agirre;Iñigo Lopez-Gazpio

  • SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity

    Eneko Agirre;Daniel Cer;Mona Diab;Aitor Gonzalez-Agirre

  • Bilingual Word Embeddings for Phrase-Based Machine Translation

    Will Y. Zou;Richard Socher;Daniel Cer;Christopher D. Manning

  • SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation

    Eneko Agirre;Carmen Banea;Daniel M. Cer;Mona T. Diab

  • SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability

    Eneko Agirre;Carmen Banea;Claire Cardie;Daniel Cer

  • SemEval-2014 Task 10: Multilingual Semantic Textual Similarity

    Eneko Agirre;Carmen Banea;Claire Cardie;Daniel Cer

  • *SEM 2013 shared task: Semantic Textual Similarity

    Eneko Agirre;Daniel Cer;Mona Diab;Aitor Gonzalez-Agirre

  • Gemma: Open Models Based on Gemini Research and Technology

    Unknown

  • Language-agnostic BERT Sentence Embedding

    Fangxiaoyu Feng;Yinfei Yang;Daniel Cer;Naveen Arivazhagan

  • Multilingual Universal Sentence Encoder for Semantic Retrieval

    Yinfei Yang;Daniel Cer;Amin Ahmad;Mandy Guo

  • Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

    Jianmo Ni;Gustavo Hernández Ábrego;Noah Constant;Ji Ma

  • Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy.

    Daniel M. Cer;Marie-Catherine de Marneffe;Daniel Jurafsky;Christopher D. Manning

  • Language-agnostic BERT Sentence Embedding

    Unknown

  • Learning Semantic Textual Similarity from Conversations

    Yinfei Yang;Steve Yuan;Daniel Cer;Sheng-yi Kong

  • Learning to recognize features of valid textual entailments

    Bill MacCartney;Trond Grenager;Marie-Catherine de Marneffe;Daniel Cer

  • SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation

    Unknown

  • SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer.

    Tu Vu;Brian Lester;Noah Constant;Rami Al-Rfou

  • Effective Parallel Corpus Mining using Bilingual Sentence Embeddings

    Mandy Guo;Qinlan Shen;Yinfei Yang;Heming Ge

  • Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model

    Muthuraman Chidambaram;Yinfei Yang;Daniel Cer;Steve Yuan

  • Learning Alignments and Leveraging Natural Logic

    Nathanael Chambers;Daniel Cer;Trond Grenager;David Hall

  • Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax.

    Yinfei Yang;Gustavo Hernandez Abrego;Steve Yuan;Mandy Guo

Frequent Co-Authors

Christopher D. Manning
Christopher D. Manning Stanford University
Eneko Agirre
Eneko Agirre University of the Basque Country
Dan Jurafsky
Dan Jurafsky Stanford University
Mona Diab
Mona Diab Carnegie Mellon University
Michel Galley
Michel Galley Microsoft (United States)
Eric Darve
Eric Darve Stanford University
Marie-Catherine de Marneffe
Marie-Catherine de Marneffe The Ohio State University
Rada Mihalcea
Rada Mihalcea University of Michigan–Ann Arbor
German Rigau
German Rigau University of the Basque Country
Janyce Wiebe
Janyce Wiebe University of Pittsburgh

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