World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
9069
World Ranking
8696
National Ranking
3728

Matt Gardner 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 Matt Gardner 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: 126 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Matt Gardner 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 Matt Gardner 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Matt Gardner is affiliated with the Allen Institute for Artificial Intelligence in the United States. Their research primarily focuses on computer science, with a significant volume of work in artificial intelligence and related subfields.

The main fields of study covered in Gardner's publications include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Epidemiology
  • Information Systems
  • Surgery

Gardner's research topics span a diverse range of areas within computational methods and machine learning, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Advanced Text Analysis Techniques
  • Domain Adaptation and Few-Shot Learning
  • Speech and Dialogue Systems

The scientist has published extensively in various venues, with frequent appearances in:

  • arXiv (Cornell University)
  • 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)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Among recent publications involving Matt Gardner as an author are:

  • "Competency Problems: On Finding and Removing Artifacts in Language Data," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "Evaluating Models' Local Decision Boundaries via Contrast Sets," 2020, arXiv (Cornell University)

Other significant papers coauthored by Gardner with colleagues include:

  • "Break It Down: A Question Understanding Benchmark," 2020, Transactions of the Association for Computational Linguistics
  • "ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension," 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "Impact of Pretraining Term Frequencies on Few-Shot Reasoning," 2022, arXiv (Cornell University)

Frequent coauthors collaborating with Gardner include:

  • Sameer Singh
  • Pradeep Dasigi
  • Jonathan Berant
  • Dheeru Dua
  • Sanjay Subramanian

Best Publications

  • Deep contextualized word representations

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

  • Deep contextualized word representations

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

  • AllenNLP: A Deep Semantic Natural Language Processing Platform

    Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord

  • Never-ending learning

    T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar

  • Never-ending learning

    T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar

  • Linguistic Knowledge and Transferability of Contextual Representations

    Nelson F. Liu;Matt Gardner;Yonatan Belinkov;Matthew E. Peters

  • Universal Adversarial Triggers for Attacking and Analyzing NLP

    Eric Wallace;Shi Feng;Nikhil Kandpal;Matt Gardner

  • AllenNLP: A Deep Semantic Natural Language Processing Platform

    Matt Gardner;Joel Grus;Mark Neumann;Oyvind Tafjord

  • DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

    Dheeru Dua;Yizhong Wang;Pradeep Dasigi;Gabriel Stanovsky

  • Simple and Effective Multi-Paragraph Reading Comprehension

    Christopher Clark;Matt Gardner

  • Evaluating Models’ Local Decision Boundaries via Contrast Sets

    Matt Gardner;Yoav Artzi;Victoria Basmov;Jonathan Berant

  • Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

    Jayant Krishnamurthy;Pradeep Dasigi;Matt Gardner

  • Do NLP Models Know Numbers? Probing Numeracy in Embeddings

    Eric Wallace;Yizhong Wang;Sujian Li;Sameer Singh

  • Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction

    Matt Gardner;Tom Mitchell

  • DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

    Unknown

  • Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases

    Matt Gardner;Partha Talukdar;Jayant Krishnamurthy;Tom Mitchell

  • Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

    Robert L. Logan;Nelson F. Liu;Matthew E. Peters;Matt Gardner

  • Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing.

    Ben Bogin;Jonathan Berant;Matt Gardner

  • Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

    Pradeep Dasigi;Nelson F. Liu;Ana Marasović;Noah A. Smith

  • Compositional Questions Do Not Necessitate Multi-hop Reasoning.

    Sewon Min;Eric Wallace;Sameer Singh;Matt Gardner

  • Crowdsourcing Multiple Choice Science Questions

    Johannes Welbl;Nelson F. Liu;Matt Gardner

  • AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

    Eric Wallace;Jens Tuyls;Junlin Wang;Sanjay Subramanian

  • Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues

    Matt Gardner;Partha Pratim Talukdar;Bryan Kisiel;Tom Mitchell

  • Break It Down: A Question Understanding Benchmark

    Tomer Wolfson;Tomer Wolfson;Mor Geva;Mor Geva;Ankit Gupta;Matt Gardner

  • Global Reasoning over Database Structures for Text-to-SQL Parsing

    Ben Bogin;Matt Gardner;Jonathan Berant

  • Reasoning Over Paragraph Effects in Situations

    Kevin Lin;Oyvind Tafjord;Peter Clark;Matt Gardner

  • Neural Module Networks for Reasoning over Text

    Nitish Gupta;Kevin Lin;Dan Roth;Sameer Singh

  • Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning

    Unknown

  • ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension

    Unknown

  • Evaluating Models' Local Decision Boundaries via Contrast Sets.

    Matt Gardner;Yoav Artzi;Victoria Basmova;Jonathan Berant

  • A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

    Pradeep Dasigi;Kyle Lo;Iz Beltagy;Arman Cohan

Frequent Co-Authors

Sameer Singh
Sameer Singh University of California, Irvine
Jonathan Berant
Jonathan Berant Tel Aviv University
Noah A. Smith
Noah A. Smith University of Washington
Hannaneh Hajishirzi
Hannaneh Hajishirzi University of Washington
Luke Zettlemoyer
Luke Zettlemoyer University of Washington
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Partha Pratim Talukdar
Partha Pratim Talukdar Indian Institute of Science
Dan Roth
Dan Roth University of Pennsylvania
Anthony Chen
Anthony Chen Hong Kong Polytechnic University
Maarten Sap
Maarten Sap Carnegie Mellon University

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