World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
100
Citations
61142
World Ranking
360
National Ranking
196

David Heckerman 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 David Heckerman 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: 338 publications — 80th percentile

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

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

David Heckerman 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 David Heckerman 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: 100 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.

Research.com Recognitions

  • 2011 - ACM Fellow For contributions to reasoning and decision-making under uncertainty.
  • 2001 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to reasoning and learning under uncertainty.

Overview

David Heckerman is affiliated with Microsoft in the United States. Their research primarily focuses on computer science, with particular attention to artificial intelligence and statistics and probability. The subfields of their study also include infectious diseases, neurology, and molecular biology.

The scientist has contributed extensively to topics such as Bayesian modeling and causal inference, statistical methods and Bayesian inference, and machine learning and data classification. Their work also covers subjects related to the long-term effects of COVID-19, SARS-CoV-2 and COVID-19 research, advanced statistical methods and models, and clinical research studies on COVID-19.

David Heckerman's frequent publication venues include:

  • arXiv (Cornell University)
  • Statistical Analysis and Data Mining The ASA Data Science Journal
  • UNC Libraries
  • Regular and Young Investigator Award Abstracts
  • JAMA Network Open

Among their recent papers are:

  • "Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions," 2021, arXiv (Cornell University)
  • "Acute and Postacute COVID-19 Outcomes Among Immunologically Naive Adults During Delta vs Omicron Waves," 2023, JAMA Network Open
  • "Likelihoods and Parameter Priors for Bayesian Networks," 2021, arXiv (Cornell University)
  • "Debiasing Concept-based Explanations with Causal Analysis," 2020, arXiv (Cornell University)
  • "Acute and Post-Acute COVID-19 Outcomes Among Immunologically Naïve Adults During Delta Versus Omicron Waves," 2022, bioRxiv (Cold Spring Harbor Laboratory)

The scientist has collaborated frequently with Mohammad Taha Bahadori, Antje Heit, Jia Li, Guang Cheng, and Ranjan Maitra.

David Heckerman has received the ACM Fellow award in 2011 for contributions to reasoning and decision-making under uncertainty. They were also named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2001 for significant contributions to reasoning and learning under uncertainty.

Best Publications

  • Empirical analysis of predictive algorithms for collaborative filtering

    John S. Breese;David Heckerman;Carl Kadie

  • Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

    David Heckerman;Dan Geiger;David M. Chickering

  • A Tutorial on Learning with Bayesian Networks.

    David Heckerman

  • A Bayesian Approach to Filtering Junk E-Mail

    Mehran Sahami;Susan Dumais;David Heckerman;Eric Horvitz

  • Inductive learning algorithms and representations for text categorization

    Susan Dumais;John Platt;David Heckerman;Mehran Sahami

  • FaST linear mixed models for genome-wide association studies.

    Christoph Lippert;Jennifer Listgarten;Ying Liu;Carl M Kadie

  • Bayesian Networks for Data Mining

    David Heckerman

  • A technique which utilizes a probabilistic classifier to detect "junk" e-mail

    Eric Horvitz;David E. Heckerman;Susan T. Dumais;Mehran Sahami

  • The lumière project: Bayesian user modeling for inferring the goals and needs of software users

    Eric Horvitz;Jack Breese;David Heckerman;David Hovel

  • An MDP-Based Recommender System

    Guy Shani;David Heckerman;Ronen I. Brafman

  • Inference system and inference engine

    John S Breese;David E Heckerman;Samuel D Hobson;Eric Horvitz

  • Large-Sample Learning of Bayesian Networks is NP-Hard

    David Maxwell Chickering;David Heckerman;Christopher Meek

  • Dependency networks for inference, collaborative filtering, and data visualization

    David Heckerman;David Maxwell Chickering;Christopher Meek;Robert Rounthwaite

  • Systems and methods for allocating placement of content items on a rendered page based upon bid value

    David Chickering;Christopher Meek;David Heckerman;Brian Burdick

  • Toward normative expert systems: Part I. The Pathfinder project.

    D E Heckerman;E J Horvitz;B N Nathwani

  • Learning Gaussian networks

    Dan Geiger;David Heckerman

  • Probabilistic interpretations for MYCIN's certainty factors

    David Heckerman

  • Real-world applications of Bayesian networks

    David Heckerman;Abe Mamdani;Michael P. Wellman

  • Collaborative filtering utilizing a belief network

    David E. Heckerman;John S. Breese;Eric Horvitz;David Maxwell Chickering

  • Causal independence for probability assessment and inference using Bayesian networks

    D. Heckerman;J.S. Breese

  • Probabilistic Independence Networks for Hidden Markov Probability Models

    Padhraic Smyth;Padhraic Smyth;David Heckerman;Michael I. Jordan

  • Bayesian networks

    David Heckerman;Michael P. Wellman

  • Large-sample learning of bayesian networks is NP-hard

    David Maxwell Chickering;Christopher Meek;David Heckerman

Frequent Co-Authors

Bruce D. Walker
Bruce D. Walker Harvard University
Philip J. R. Goulder
Philip J. R. Goulder University of Oxford
Jonathan M. Carlson
Jonathan M. Carlson Microsoft (United States)
David Maxwell Chickering
David Maxwell Chickering Microsoft (United States)
Eric Horvitz
Eric Horvitz Microsoft (United States)
Jennifer Listgarten
Jennifer Listgarten University of California, Berkeley
Zabrina L. Brumme
Zabrina L. Brumme Simon Fraser University
Dan Geiger
Dan Geiger Technion – Israel Institute of Technology
Thumbi Ndung'u
Thumbi Ndung'u University of KwaZulu-Natal
Nebojsa Jojic
Nebojsa Jojic Microsoft (United States)

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