World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
7693
World Ranking
9227
National Ranking
3929

Michael J. Paul 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 Michael J. Paul 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: 124 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Michael J. Paul 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 Michael J. Paul 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Michael J. Paul is affiliated with the University of Colorado Boulder in the United States. Their research primarily spans the field of Biochemistry, Genetics and Molecular Biology with a focus on several subfields including Molecular Biology, Cellular and Molecular Neuroscience, Cell Biology, Genetics, and Oncology.

Their recent publications cover a range of topics and include the following papers:

  • You Are What You Tweet: Analyzing Twitter for Public Health, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes, 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • Ligand bias in receptor tyrosine kinase signaling, 2020, Journal of Biological Chemistry
  • Quantifying the strength of heterointeractions among receptor tyrosine kinases from different subfamilies: Implications for cell signaling, 2020, Journal of Biological Chemistry
  • Probing Membrane Protein Association Using Concentration-Dependent Number and Brightness, 2020, Angewandte Chemie International Edition

Their frequent publication venues include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Biological Chemistry
  • Nature Communications
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Angewandte Chemie International Edition

Coauthors who have collaborated often with Michael J. Paul are:

  • Natalia Jura
  • Kalina Hristova
  • Hayarpi Torosyan
  • Megan Lo
  • Kliment A. Verba

The main topics covered in their research consist of:

  • Axon Guidance and Neuronal Signaling
  • Receptor Mechanisms and Signaling
  • HER2/EGFR in Cancer Research
  • Lipid Membrane Structure and Behavior
  • Hedgehog Signaling Pathway Studies
  • RNA and protein synthesis mechanisms
  • Protein Kinase Regulation and GTPase Signaling

Best Publications

  • You Are What You Tweet: Analyzing Twitter for Public Health

    Michael J. Paul;Mark Dredze

  • National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic

    David A. Broniatowski;Michael J. Paul;Mark Dredze

  • Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

    Mauricio Santillana;André T. Nguyen;Mark Dredze;Michael J. Paul

  • Separating Fact from Fear: Tracking Flu Infections on Twitter

    Alex Lamb;Michael J. Paul;Mark Dredze

  • Discovering health topics in social media using topic models.

    Michael J. Paul;Mark Dredze

  • Twitter improves influenza forecasting.

    Michael J. Paul;Mark Dredze;David Broniatowski

  • Carmen: A Twitter Geolocation System with Applications to Public Health

    Mark Dredze;Michael J. Paul;Shane Bergsma;Hieu Tran

  • Enhancing disease surveillance with novel data streams: challenges and opportunities

    Benjamin M. Althouse;Samuel V. Scarpino;Lauren Ancel Meyers;Lauren Ancel Meyers;John W. Ayers

  • Overview of the IWSLT 2010 evaluation campaign.

    Michael Paul;Marcello Federico;Sebastian Stüker

  • Summarizing Contrastive Viewpoints in Opinionated Text

    Michael Paul;ChengXiang Zhai;Roxana Girju

  • A two-dimensional topic-aspect model for discovering multi-faceted topics

    Michael Paul;Roxana Girju

  • Social media mining for public health monitoring and surveillance

    Michael J. Paul;Abeed Sarker;John S. Brownstein;Azadeh Nikfarjam

  • A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews

    Byron C Wallace;Michael J Paul;Urmimala Sarkar;Thomas A Trikalinos

  • Social media as a sensor of air quality and public response in China.

    Shiliang Wang;Michael J Paul;Mark Dredze

  • Cross-Cultural Analysis of Blogs and Forums with Mixed-Collection Topic Models

    Michael Paul;Roxana Girju

  • Identifying and Categorizing Disaster-Related Tweets.

    Kevin Stowe;Michael J. Paul;Martha Palmer;Leysia Palen

  • Social Monitoring for Public Health

    Michael J. Paul;Mark Dredze;Gary Marchionini

  • Hierarchical Bayesian Models for Latent Attribute Detection in Social Media.

    Delip Rao;Michael J. Paul;Clayton Fink;David Yarowsky

  • Overview of the IWSLT04 Evaluation Campaign

    Yasuhiro Akiba;Marcello Federico;Noriko Kando;Hiromi Nakaiwa

  • Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019

    Davy Weissenbacher;Abeed Sarker;Arjun Magge;Ashlynn Daughton

Frequent Co-Authors

Mark Dredze
Mark Dredze Johns Hopkins University
Eiichiro Sumita
Eiichiro Sumita National Institute of Information and Communications Technology
Jordan Boyd-Graber
Jordan Boyd-Graber University of Maryland, College Park
John S. Brownstein
John S. Brownstein Boston Children's Hospital
Byron C. Wallace
Byron C. Wallace Northeastern University
Satoshi Nakamura
Satoshi Nakamura Nara Institute of Science and Technology
Marcello Federico
Marcello Federico Amazon (United States)
Ryen W. White
Ryen W. White Microsoft (United States)
Eric Horvitz
Eric Horvitz Microsoft (United States)
John W. Peabody
John W. Peabody University of California, San Francisco

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