World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
32341
World Ranking
4175
National Ranking
164

Colin Raffel 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 Colin Raffel 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 102 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Colin Raffel 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 Colin Raffel sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Colin Raffel is affiliated with the University of Toronto in Canada and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and machine learning. Their research output includes over 150 publications, reflecting a broad engagement with key areas of computational study.

The primary fields of study represented in their work include:

  • Computer Science

Within this overarching field, Raffel's research spans several subfields, notably:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Health Informatics
  • Statistical and Nonlinear Physics

The main topics covered in Raffel's publications encompass:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Machine Learning and Algorithms

Raffel's frequent coauthors include:

  • Mohit Bansal
  • Derek Tam
  • Teven Le Scao
  • Sharan Narang
  • Adam P. Roberts

Several key venues have published Raffel's work multiple times, illustrating a sustained engagement with prominent outlets. These venues are:

  • arXiv (Cornell University)
  • UNC Libraries
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Communications of the ACM

Notable recent papers authored or coauthored by Raffel include:

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence, 2020, arXiv (Cornell University)
  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • Emergent Abilities of Large Language Models, 2022, arXiv (Cornell University)
  • Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options, 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • mT5: A massively multilingual pre-trained text-to-text transformer, 2020, arXiv (Cornell University)

Best Publications

  • Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

    Colin Raffel;Noam Shazeer;Adam Roberts;Katherine Lee

  • librosa: Audio and Music Signal Analysis in Python

    Brian McFee;Colin Raffel;Dawen Liang;Daniel P.W. Ellis

  • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

    Kihyuk Sohn;David Berthelot;Chun-Liang Li;Zizhao Zhang

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • MixMatch: A Holistic Approach to Semi-Supervised Learning

    David Berthelot;Nicholas Carlini;Ian Goodfellow;Nicolas Papernot

  • Emergent Abilities of Large Language Models

    Unknown

  • mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

    Linting Xue;Noah Constant;Adam Roberts;Mihir Kale

  • BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    Unknown

  • Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

    Avital Oliver;Augustus Odena;Colin A. Raffel;Ekin Dogus Cubuk

  • How Much Knowledge Can You Pack Into the Parameters of a Language Model

    Adam Roberts;Colin Raffel;Noam Shazeer

  • Multitask Prompted Training Enables Zero-Shot Task Generalization

    Victor Sanh;Albert Webson;Colin Raffel;Stephen H. Bach

  • Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

    Unknown

  • Crosslingual Generalization through Multitask Finetuning

    Unknown

  • Thermometer Encoding: One Hot Way To Resist Adversarial Examples

    Jacob Buckman;Aurko Roy;Colin Raffel;Ian Goodfellow

  • MIR_EVAL: A Transparent Implementation of Common MIR Metrics.

    Colin Raffel;Brian McFee;Eric J. Humphrey;Justin Salamon

  • ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring

    David Berthelot;Nicholas Carlini;Ekin D. Cubuk;Alex Kurakin

  • Extracting Training Data from Large Language Models

    Nicholas Carlini;Florian Tramèr;Eric Wallace;Matthew Jagielski

  • A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

    Adam Roberts;Jesse H. Engel;Colin Raffel;Curtis Hawthorne

  • PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

    Unknown

  • Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems

    Colin Raffel;Daniel P. W. Ellis

  • Lasagne: First release.

    Sander Dieleman;Michael Heilman;Jack Kelly;Martin Thoma

  • Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition.

    Yao Qin;Nicholas Carlini;Garrison W. Cottrell;Ian J. Goodfellow

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

    David Berthelot;Nicholas Carlini;Ekin D. Cubuk;Alex Kurakin

  • Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching

    Colin Raffel

  • Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

    Yao Qin;Nicholas Carlini;Ian Goodfellow;Garrison Cottrell

Frequent Co-Authors

Daniel P. W. Ellis
Daniel P. W. Ellis Google (United States)
Ian Goodfellow
Ian Goodfellow Google (United States)
Nicholas Carlini
Nicholas Carlini Google (United States)
Douglas Eck
Douglas Eck Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Ekin D. Cubuk
Ekin D. Cubuk Google (United States)
Noam Shazeer
Noam Shazeer Google (United States)
Garrison W. Cottrell
Garrison W. Cottrell University of California, San Diego
Kihyuk Sohn
Kihyuk Sohn Google (United States)

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