World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
95
Citations
41752
World Ranking
459
National Ranking
252

Raymond J. Mooney 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 Raymond J. Mooney 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: 301 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Raymond J. Mooney 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 Raymond J. Mooney 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: 95 D-Index — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Overview

Raymond J. Mooney is affiliated with The University of Texas at Austin in the United States. Their research contributions are primarily situated within the field of Computer Science, with a significant focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Control and Systems Engineering, and Software.

The research topics covered by Raymond J. Mooney span several areas including:

  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Natural Language Processing Techniques
  • Software Engineering Research
  • Speech and dialogue systems
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques

Their publication record includes a mix of journal articles, conference papers, and preprints, with frequent venues including arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Computer Speech & Language, and the Journal of Artificial Intelligence Research.

Some recent papers by Raymond J. Mooney include:

  • Spoken language interaction with robots: Recommendations for future research, 2021, Computer Speech & Language
  • Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog, 2020, Journal of Artificial Intelligence Research
  • PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards, 2020, arXiv (Cornell University)
  • Improving VQA and its Explanations by Comparing Competing Explanations, 2020, arXiv (Cornell University)
  • Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval, 2024, arXiv (Cornell University)

They have collaborated frequently with several co-authors, most notably:

  • Milos Gligoric
  • Junyi Jessy Li
  • Sheena Panthaplackel
  • Stefanie Tellex
  • Matthew Marge

This body of work illustrates an emphasis on integrating language understanding with machine learning techniques, as well as interdisciplinary applications involving speech, vision, and robotics. The diversity of research topics and publication venues reflects a broad engagement with multiple aspects of artificial intelligence and computer science.

Best Publications

  • Content-based book recommending using learning for text categorization

    Raymond J. Mooney;Loriene Roy

  • Sequence to Sequence -- Video to Text

    Subhashini Venugopalan;Marcus Rohrbach;Jeffrey Donahue;Raymond Mooney

  • Explanation-Based Learning: An Alternative View

    Gerald Dejong;Raymond Mooney

  • Adaptive duplicate detection using learnable string similarity measures

    Mikhail Bilenko;Raymond J. Mooney

  • Semi-supervised Clustering by Seeding

    Sugato Basu;Arindam Banerjee;Raymond J. Mooney

  • A Shortest Path Dependency Kernel for Relation Extraction

    Razvan Bunescu;Raymond Mooney

  • Integrating constraints and metric learning in semi-supervised clustering

    Mikhail Bilenko;Sugato Basu;Raymond J. Mooney

  • A probabilistic framework for semi-supervised clustering

    Sugato Basu;Mikhail Bilenko;Raymond J. Mooney

  • Impact of Similarity Measures on Web-page Clustering

    Alexander Strehl;Joydeep Ghosh;Raymond Mooney

  • Translating Videos to Natural Language Using Deep Recurrent Neural Networks

    Subhashini Venugopalan;Huijuan Xu;Jeff Donahue;Marcus Rohrbach

  • Semi-supervised graph clustering: a kernel approach

    Brian Kulis;Sugato Basu;Inderjit Dhillon;Raymond Mooney

  • Active Semi-Supervision for Pairwise Constrained Clustering

    Sugato Basu;Arindam Banerjee;Raymond J. Mooney

  • Relational learning of pattern-match rules for information extraction

    Mary Elaine Califf;Raymond J. Mooney

  • Learning to parse database queries using inductive logic programming

    John M. Zelle;Raymond J. Mooney

  • Adaptive name matching in information integration

    M. Bilenko;R. Mooney;W. Cohen;P. Ravikumar

  • Subsequence Kernels for Relation Extraction

    Raymond J. Mooney;Razvan C. Bunescu

  • YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition

    Sergio Guadarrama;Niveda Krishnamoorthy;Girish Malkarnenkar;Subhashini Venugopalan

  • Comparative experiments on learning information extractors for proteins and their interactions

    Razvan Bunescu;Ruifang Ge;Rohit J. Kate;Edward M. Marcotte

  • Learning to interpret natural language navigation instructions from observations

    David L. Chen;Raymond J. Mooney

  • Symbolic and neural learning algorithms: an experimental comparison

    Jude W. Shavlik;Raymond J. Mooney;Geoffrey G. Towell

Frequent Co-Authors

Kate Saenko
Kate Saenko Boston University
Razvan Bunescu
Razvan Bunescu University of North Carolina at Charlotte
Peter Stone
Peter Stone The University of Texas at Austin
Katrin Erk
Katrin Erk The University of Texas at Austin
Marcus Rohrbach
Marcus Rohrbach Facebook (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Hwee Tou Ng
Hwee Tou Ng National University of Singapore
Joydeep Ghosh
Joydeep Ghosh The University of Texas at Austin
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Edward M. Marcotte
Edward M. Marcotte The University of Texas at Austin

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