World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
22578
World Ranking
3739
National Ranking
1786

Gregory F. Cooper 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 Gregory F. Cooper 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: 262 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Gregory F. Cooper 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 Gregory F. Cooper 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: 57 D-Index — 74th percentile

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

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

Overview

Gregory F. Cooper is affiliated with the University of Pittsburgh in the United States. Their research spans multiple disciplines, including Medicine, Computer Science, and Biochemistry, Genetics and Molecular Biology. This interdisciplinary focus is reflected in their work that integrates computational methods with health sciences.

Cooper's research covers major subfields such as Artificial Intelligence, Molecular Biology, Health Information Management, Epidemiology, and Surgery. The scientist's main topics of study include Bayesian Modeling and Causal Inference, Electronic Health Records Systems, Machine Learning in Healthcare, Healthcare Technology and Patient Monitoring, Artificial Intelligence in Healthcare, Bioinformatics and Genomic Networks, and Cancer Genomics and Diagnostics.

Some of Gregory F. Cooper's recent papers include:

  • Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study (2020, Journal of Medical Internet Research)
  • Heterogeneity in the Effect of Early Goal-Directed Therapy for Septic Shock: A Secondary Analysis of Two Multicenter International Trials (2024, Critical Care Medicine)
  • A Bayesian approach for detecting a disease that is not being modeled (2020, PLoS ONE)
  • Graphical Presentations of Clinical Data in a Learning Electronic Medical Record (2020, Applied Clinical Informatics)
  • Evaluation of eye tracking for a decision support application (2021, JAMIA Open)

Frequent co-authors collaborating with Cooper include:

  • Harry Hochheiser
  • Shyam Visweswaran
  • Xinghua Lu
  • Andrew J. King
  • Gilles Clermont

The scientist frequently publishes in venues such as bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), JAMIA Open, Pain, and SSRN Electronic Journal, indicating active engagement with platforms centered on biomedical informatics, computational biology, and medical research.

Best Publications

  • A Bayesian Method for the Induction of Probabilistic Networks from Data

    Gregory F. Cooper;Edward Herskovits

  • The computational complexity of probabilistic inference using Bayesian belief networks (research note)

    Gregory F. Cooper

  • A simple algorithm for identifying negated findings and diseases in discharge summaries

    Wendy Webber Chapman;Will Bridewell;Paul Hanbury;Gregory F. Cooper

  • The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks

    Ingo A. Beinlich;Henri Jacques Suermondt;R. Martin Chavez;Gregory F. Cooper

  • Obtaining well calibrated probabilities using bayesian binning

    Mahdi Pakdaman Naeini;Gregory F. Cooper;Milos Hauskrecht

  • Computation, Causation, and Discovery

    Clark N. Glymour;Gregory Floyd Cooper

  • Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

    M A Shwe;B Middleton;D E Heckerman;M Henrion

  • A Bayesian method for constructing Bayesian belief networks from databases

    Gregory F. Cooper;Edward Herskovits

  • A Bayesian Approach to Causal Discovery

    David Heckerman;Christopher Meek;Gregory Cooper

  • An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality

    Gregory F. Cooper;Constantin F. Aliferis;Richard Ambrosino;John M. Aronis

  • NESTOR: A Computer-Based Medical Diagnostic Aid That Integrates Causal and Probabilistic Knowledge.

    G F Cooper

  • Causal discovery from a mixture of experimental and observational data

    Gregory F. Cooper;Changwon Yoo

  • Bayesian network anomaly pattern detection for disease outbreaks

    Weng-Keen Wong;Andrew Moore;Gregory Cooper;Michael Wagner

  • A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

    Gregory F. Cooper

  • Reflection and action under scarce resources: theoretical principles and empirical study

    Eric J. Horvitz;Gregory F. Cooper;David E. Heckerman

  • Evaluation of negation phrases in narrative clinical reports.

    Wendy W. Chapman;Will Bridewell;Paul Hanbury;Gregory F. Cooper

  • Accelerating U.S. EHR adoption: How to get there from here. Recommendations based on the 2004 ACMI retreat

    Blackford Middleton;W. Ed Hammond;Patricia F. Brennan;Gregory F. Cooper

  • Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

    Michael Q. Ding;Lujia Chen;Gregory F. Cooper;Jonathan D. Young

  • Rule-based anomaly pattern detection for detecting disease outbreaks

    Weng-Keen Wong;Andrew Moore;Gregory Cooper;Michael Wagner

  • an entropy-driven system for construction of probabilistic expert systems from databases

    Edward Herskovits;Gregory F. Cooper

  • A Method for Using Belief Networks as Influence Diagrams

    Gregory F. Cooper

  • Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

    Gregory F. Cooper;Serafín Moral

Frequent Co-Authors

Michael Wagner
Michael Wagner TU Dresden
Bruce G. Buchanan
Bruce G. Buchanan University of Pittsburgh
Constantin F. Aliferis
Constantin F. Aliferis University of Minnesota
Wendy W. Chapman
Wendy W. Chapman University of Melbourne
Peter J. Haug
Peter J. Haug University of Utah
David Heckerman
David Heckerman Microsoft (United States)
Weng-Keen Wong
Weng-Keen Wong Oregon State University
Eric Horvitz
Eric Horvitz Microsoft (United States)
Andrew W. Moore
Andrew W. Moore Carnegie Mellon University
Randolph A. Miller
Randolph A. Miller Vanderbilt University

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