World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
113
Citations
87975
World Ranking
196
National Ranking
113

Luke Zettlemoyer 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 Luke Zettlemoyer 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: 286 publications — 71st percentile

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

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

Luke Zettlemoyer 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 Luke Zettlemoyer 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: 113 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Overview

Luke Zettlemoyer is affiliated with the University of Washington in the United States. The primary field of their research is Computer Science, with a strong focus on subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Electrical and Electronic Engineering.

The main topics covered in their work include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Speech Recognition and Synthesis
  • Domain Adaptation and Few-Shot Learning
  • Software Engineering Research

Significant recent papers authored or coauthored by Luke Zettlemoyer include:

  • Towards Learning Terminological Concept Systems from Multilingual Natural Language Text, 2021, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Multilingual Denoising Pre-training for Neural Machine Translation, 2020, Transactions of the Association for Computational Linguistics
  • Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs, 2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Multilingual Denoising Pre-training for Neural Machine Translation, 2020, arXiv (Cornell University)
  • QLoRA: Efficient Finetuning of Quantized LLMs, 2023, arXiv (Cornell University)

Frequent coauthors who have collaborated with Luke Zettlemoyer include:

  • Hannaneh Hajishirzi
  • Michael Lewis
  • Sewon Min
  • Noah A. Smith
  • Marjan Ghazvininejad

Luke Zettlemoyer has published extensively in several venues, with major contributions found in:

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

Best Publications

  • RoBERTa: A Robustly Optimized BERT Pretraining Approach

    Yinhan Liu;Myle Ott;Naman Goyal;Jingfei Du

  • Deep contextualized word representations

    Matthew E. Peters;Mark Neumann;Mohit Iyyer;Matt Gardner

  • 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

  • SpanBERT: Improving Pre-training by Representing and Predicting Spans

    Mandar Joshi;Danqi Chen;Yinhan Liu;Daniel S. Weld

  • TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

    Mandar Joshi;Eunsol Choi;Daniel S. Weld;Luke Zettlemoyer

  • Multilingual Denoising Pre-training for Neural Machine Translation

    Yinhan Liu;Jiatao Gu;Naman Goyal;Xian Li

  • AllenNLP: A Deep Semantic Natural Language Processing Platform

    Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord

  • Learning to map sentences to logical form: structured classification with probabilistic categorial grammars

    Luke S. Zettlemoyer;Michael Collins

  • Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

    Unknown

  • Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

    Raphael Hoffmann;Congle Zhang;Xiao Ling;Luke Zettlemoyer

  • End-to-end Neural Coreference Resolution

    Kenton Lee;Luheng He;Mike Lewis;Luke Zettlemoyer

  • Toolformer: Language Models Can Teach Themselves to Use Tools

    Unknown

  • QLoRA: Efficient Finetuning of Quantized LLMs

    Unknown

  • QuAC: Question Answering in Context

    Eunsol Choi;He He;Mohit Iyyer;Mohit Iyyer;Mark Yatskar

  • Summarizing Source Code using a Neural Attention Model

    Srinivasan Iyer;Ioannis Konstas;Alvin Cheung;Luke Zettlemoyer

  • Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

    Mohit Iyyer;John Wieting;Kevin Gimpel;Luke Zettlemoyer

  • Dissecting Contextual Word Embeddings: Architecture and Representation

    Matthew E. Peters;Mark Neumann;Luke Zettlemoyer;Wen-tau Yih

  • Higher-Order Coreference Resolution with Coarse-to-Fine Inference

    Kenton Lee;Luheng He;Luke Zettlemoyer

  • Mask-Predict: Parallel Decoding of Conditional Masked Language Models.

    Marjan Ghazvininejad;Omer Levy;Yinhan Liu;Luke Zettlemoyer

  • Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

    Yoav Artzi;Luke Zettlemoyer

  • Open question answering over curated and extracted knowledge bases

    Anthony Fader;Luke Zettlemoyer;Oren Etzioni

  • Zero-Shot Relation Extraction via Reading Comprehension

    Omer Levy;Minjoon Seo;Eunsol Choi;Luke Zettlemoyer

Frequent Co-Authors

Omer Levy
Omer Levy Deep Mind
Kenton Lee
Kenton Lee Google (United States)
Hannaneh Hajishirzi
Hannaneh Hajishirzi University of Washington
Yejin Choi
Yejin Choi Stanford University
Daniel S. Weld
Daniel S. Weld University of Washington
Yoav Artzi
Yoav Artzi Cornell University
Matt Gardner
Matt Gardner Allen Institute for Artificial Intelligence
Danqi Chen
Danqi Chen Princeton University
Mohit Iyyer
Mohit Iyyer University of Maryland, College Park

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