World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
62
Citations
20703
World Ranking
2845
National Ranking
1407

Philip Resnik 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 Philip Resnik 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: 193 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Philip Resnik 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 Philip Resnik 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: 62 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Overview

Philip Resnik is affiliated with the University of Maryland, College Park, in the United States. Their research spans across multiple domains within computer science, psychology, and social sciences, reflecting a multidisciplinary approach to understanding and analyzing complex social and cognitive phenomena.

The main fields of study in Resnik's work include:

  • Computer Science
  • Psychology
  • Social Sciences

Within these areas, their subfields of interest emphasize:

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Social Psychology
  • Sociology and Political Science
  • Applied Psychology

Resnik's research topics cover a variety of contemporary and interdisciplinary themes such as:

  • Mental Health via Writing
  • Topic Modeling
  • Digital Mental Health Interventions
  • Neuroscience and Music Perception
  • Computational and Text Analysis Methods
  • Social Media and Politics
  • Neural dynamics and brain function

The scientist has contributed to multiple recent publications, including:

  • "A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis," 2021, published in PLoS ONE
  • "Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions," 2023, published in eLife
  • "The Prompt Report: A Systematic Survey of Prompt Engineering Techniques," 2024, published on arXiv (Cornell University)
  • "Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence," 2021, published on arXiv (Cornell University)
  • "Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions," 2021, published on bioRxiv (Cold Spring Harbor Laboratory)

Frequent co-authors collaborating with Resnik include:

  • Pranav Goel
  • Shohini Bhattasali
  • Alexander Hoyle
  • Deanna L. Kelly
  • Christian Brodbeck

The researcher's work is often published in venues including:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • PLoS ONE
  • SSRN Electronic Journal
  • eLife

Best Publications

  • Using information content to evaluate semantic similarity in a taxonomy

    Philip Resnik

  • Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language

    Philip Resnik

  • The Web as a parallel corpus

    Philip Resnik;Noah A. Smith

  • Selection and information: a class-based approach to lexical relationships

    Philip Stuart Resnik

  • Bootstrapping parsers via syntactic projection across parallel texts

    Rebecca Hwa;Philip Resnik;Amy Weinberg;Clara Cabezas

  • Selectional Preference and Sense Disambiguation

    Philip Resnik

  • Selectional constraints: an information-theoretic model and its computational realization

    Philip Resnik

  • Mining the Web for Bilingual Text

    Philip Resnik

  • Political Ideology Detection Using Recursive Neural Networks

    Mohit Iyyer;Peter Enns;Jordan Boyd-Graber;Philip Resnik

  • Speech recognition apparatus which predicts word classes from context and words from word classes

    Peter Fitzhugh Brown;Stephen Andrew Della Pietra;Vincent Joseph Della Pietra;Robert Leroy Mercer

  • An Unsupervised Method for Word Sense Tagging using Parallel Corpora

    Mona Diab;Philip Resnik

  • Distinguishing systems and distinguishing senses: new evaluation methods for Word Sense Disambiguation

    Philip Resnik;David Yarowsky

  • cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models

    Chris Dyer;Adam Lopez;Juri Ganitkevitch;Jonathan Weese

  • Online Large-Margin Training of Syntactic and Structural Translation Features

    David Chiang;Yuval Marton;Philip Resnik

  • Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002

    James Allan;Jay Aslam;Nicholas Belkin;Chris Buckley

  • A rule-based approach to prepositional phrase attachment disambiguation

    Eric Brill;Philip Resnik

  • Disambiguating Noun Groupings with Respect to WordNet Senses

    Philip Resnik

  • Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

    Philip Resnik;William Armstrong;Leonardo Claudino;Thang Nguyen

  • Generalizing Word Lattice Translation

    Christopher Dyer;Smaranda Muresan;Philip Resnik

  • A Perspective on Word Sense Disambiguation Methods and Their Evaluation

    Philip Resnik

  • Elements of a computational model for multi-party discourse: The turn-taking behavior of Supreme Court justices

    Timothy Hawes;Jimmy Lin;Philip Resnik

  • A Class-Based Approach to Lexical Relationships

    P. Resnik

Frequent Co-Authors

Jordan Boyd-Graber
Jordan Boyd-Graber University of Maryland, College Park
Douglas W. Oard
Douglas W. Oard University of Maryland, College Park
Hal Daumé
Hal Daumé University of Maryland, College Park
Benjamin B. Bederson
Benjamin B. Bederson University of Maryland, College Park
Chris Dyer
Chris Dyer Google (United States)
Bonnie J. Dorr
Bonnie J. Dorr University of Florida
Jimmy Lin
Jimmy Lin University of Waterloo
Mona Diab
Mona Diab Carnegie Mellon University
David Chiang
David Chiang University of Notre Dame
William Byrne
William Byrne University of Cambridge

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