World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
15100
World Ranking
10442
National Ranking
4363

Dani Yogatama 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 Dani Yogatama 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: 63 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.

Dani Yogatama 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 Dani Yogatama 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Dani Yogatama is affiliated with the University of Southern California in the United States and focuses on research primarily within the field of Computer Science. Their work spans multiple subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, General Social Sciences, and Electrical and Electronic Engineering.

Their research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Machine Learning and Algorithms
  • Expert Finding and Q&A Systems
  • Advanced Graph Neural Networks

Recent publications by Dani Yogatama demonstrate contributions to foundational and emerging topics in language and neural modeling. Notable papers include:

  • "Emergent Abilities of Large Language Models" (2022), published in arXiv (Cornell University)
  • "Random Feature Attention" (2021), published in arXiv (Cornell University)
  • "A Contrastive Framework for Neural Text Generation" (2022), published in arXiv (Cornell University)
  • "Mind the Gap: Assessing Temporal Generalization in Neural Language Models" (2021), published in arXiv (Cornell University)
  • "Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers" (2021), published in arXiv (Cornell University)

Dani Yogatama frequently collaborates with a number of researchers including:

  • Lingpeng Kong
  • Cyprien de Masson d'Autume
  • Phil Blunsom
  • Yi Tay
  • Ollie Liu

The main venues for Dani Yogatama's publications are:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Best Publications

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Deep speech 2: end-to-end speech recognition in English and mandarin

    Dario Amodei;Sundaram Ananthanarayanan;Rishita Anubhai;Jingliang Bai

  • Emergent Abilities of Large Language Models

    Unknown

  • Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

    Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das

  • On the Cross-lingual Transferability of Monolingual Representations

    Mikel Artetxe;Sebastian Ruder;Dani Yogatama

  • Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

    Dario Amodei;Rishita Anubhai;Eric Battenberg;Carl Case

  • Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

    Wang Ling;Dani Yogatama;Chris Dyer;Phil Blunsom

  • Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics

    Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das

  • Learning and Evaluating General Linguistic Intelligence.

    Dani Yogatama;Cyprien de Masson d'Autume;Jerome T. Connor;Tomás Kociský

  • Sparse Overcomplete Word Vector Representations

    Manaal Faruqui;Yulia Tsvetkov;Dani Yogatama;Chris Dyer

  • Efficient Transfer Learning Method for Automatic Hyperparameter Tuning

    Dani Yogatama;Gideon Mann

  • Generative and Discriminative Text Classification with Recurrent Neural Networks

    Dani Yogatama;Chris Dyer;Wang Ling;Phil Blunsom

  • LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better.

    Adhiguna Kuncoro;Chris Dyer;John Hale;Dani Yogatama

  • Episodic Memory in Lifelong Language Learning

    Cyprien de Masson d'Autume;Sebastian Ruder;Lingpeng Kong;Dani Yogatama

  • Random Feature Attention

    Hao Peng;Nikolaos Pappas;Dani Yogatama;Roy Schwartz

  • Learning to Compose Words into Sentences with Reinforcement Learning

    Dani Yogatama;Phil Blunsom;Chris Dyer;Edward Grefenstette

  • Embedding Methods for Fine Grained Entity Type Classification

    Dani Yogatama;Daniel Gillick;Nevena Lazic

  • Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

    Po-Sen Huang;Huan Zhang;Ray Jiang;Robert Stanforth

  • Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

    Po-Sen Huang;Robert Stanforth;Johannes Welbl;Chris Dyer

  • Predicting a Scientific Community’s Response to an Article

    Dani Yogatama;Michael Heilman;Brendan O'Connor;Chris Dyer

  • A Mutual Information Maximization Perspective of Language Representation Learning

    Lingpeng Kong;Cyprien de Masson d'Autume;Lei Yu;Wang Ling

  • Adaptive Semiparametric Language Models

    Dani Yogatama;Cyprien de Masson d'Autume;Lingpeng Kong

Frequent Co-Authors

Noah A. Smith
Noah A. Smith University of Washington
Chris Dyer
Chris Dyer Google (United States)
Phil Blunsom
Phil Blunsom University of Oxford
Sebastian Ruder
Sebastian Ruder Google (United States)
Chong Wang
Chong Wang University of Helsinki
Bryan Catanzaro
Bryan Catanzaro Nvidia (United States)
Lei Yu
Lei Yu Google (United States)
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Adam Coates
Adam Coates Apple (United States)

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