World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
7227
World Ranking
8837
National Ranking
3773

Marek J. Druzdzel 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 Marek J. Druzdzel 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: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Marek J. Druzdzel 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 Marek J. Druzdzel 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Marek J. Druzdzel is affiliated with the University of Pittsburgh in the United States. Their research primarily focuses on computer science, with a significant emphasis on artificial intelligence. The work overlaps various interdisciplinary subfields, including health information management, cardiology and cardiovascular medicine, ecology, and ecological modeling.

The scientist has contributed to multiple areas of study, prominently including:

  • Bayesian Modeling and Causal Inference
  • Artificial Intelligence in Healthcare
  • Explainable Artificial Intelligence (XAI)
  • Machine Learning in Healthcare
  • Species Distribution and Climate Change
  • Data Quality and Management
  • Pulmonary Hypertension Research and Treatments

Recent publications authored or co-authored by Druzdzel include the following papers:

  • "Risk stratification in pulmonary arterial hypertension using Bayesian analysis" (2020), published in European Respiratory Journal
  • "Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners' Performance in Computational Thinking" (2024), published in Education Sciences
  • "Bayesian network models with decision tree analysis for management of childhood malaria in Malawi" (2021), published in BMC Medical Informatics and Decision Making
  • "Using expert elicitation to identify effective combinations of management actions for koala conservation in different regional landscapes" (2022), published in Wildlife Research
  • "Latest features of the ecosystem management decision support system, version 8.0" (2023), published in Frontiers in Environmental Science

Druzdzel frequently collaborates with a consistent group of researchers, including:

  • Sanya B. Taneja
  • Gerald P. Douglas
  • Gregory F. Cooper
  • Marian G. Michaels
  • Shyam Visweswaran

The scientist's work often appears in recognized venues such as:

  • Entropy
  • Research Square (Research Square)
  • European Respiratory Journal
  • Education Sciences
  • BMC Medical Informatics and Decision Making

Best Publications

  • On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

    Cristina Conati;Abigail S. Gertner;Kurt VanLehn;Marek J. Druzdzel

  • The emerging science of very early detection of disease outbreaks.

    Michael M. Wagner;Fu-Chiang Tsui;Jeremy U. Espino;Virginia M. Dato

  • Learning Bayesian network parameters from small data sets: application of Noisy-OR gates

    Agnieszka Oniśko;Marek J. Druzdzel;Hanna Wasyluk

  • Building probabilistic networks: "Where do the numbers come from?" guest editors' introduction

    M.J. Druzdzel;L.C. van der Gaag

  • SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: a development environment for graphical decision-theoretic models

    Marek J. Druzdzel

  • AIS-BN: an adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks

    Jian Cheng;Marek J. Druzdzel

  • Elicitation of probabilities for belief networks: combining qualitative and quantitative information

    Marek J. Druzdzel;Linda C. Van Der Gaag

  • Efficient reasoning in qualitative probabilistic networks

    Marek J. Druzdzel;Max Henrion

  • Canonical Probabilistic Models for Knowledge Engineering

    Inteligencia Artiflcial;Juan del Rosal;Marek J. Druzdzel

  • Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning

    Max Henrion;Marek J. Druzdzel

  • Causality in Bayesian belief networks

    Marek J. Druzdzel;Herbert A. Simon

  • Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

    Paul Arora;Devon Boyne;Justin J. Slater;Alind Gupta

  • An importance sampling algorithm based on evidence pre-propagation

    Changhe Yuan;Marek J. Druzdzel

  • A hybrid anytime algorithm for the construction of causal models from sparse data

    Denver Dash;Marek J. Druzdzel

  • Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

    A. Zagorecki;M. J. Druzdzel

  • Probabilistic reasoning in decision support systems: from computation to common sense

    Marek Jozef Druzdzel

  • Importance sampling algorithms for Bayesian networks: Principles and performance

    Changhe Yuan;Marek J Druzdzel

  • Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

    Agnieszka Oniśko;Marek J. Druzdzel

  • Robust independence testing for constraint-based learning of causal structure

    Denver Dash;Marek J. Druzdzel

  • A comparison of structural distance measures for causal Bayesian network models

    Martijn de Jongh;Marek J. Druzdzel

  • Theoretical analysis and practical insights on importance sampling in Bayesian networks

    Changhe Yuan;Marek J. Druzdzel

  • A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data

    Denver Dash;Marek J. Druzdzel

Frequent Co-Authors

James F. Antaki
James F. Antaki Cornell University
Clark Glymour
Clark Glymour Carnegie Mellon University
Herbert A. Simon
Herbert A. Simon Carnegie Mellon University
Jayant R. Kalagnanam
Jayant R. Kalagnanam IBM (United States)
Christian Freksa
Christian Freksa University of Bremen
Jon F. Merz
Jon F. Merz University of Pennsylvania
Mitchell J. Small
Mitchell J. Small Carnegie Mellon University
Dara Sakolsky
Dara Sakolsky University of Pittsburgh
Rinad S. Beidas
Rinad S. Beidas University of Pennsylvania
Mary Hegarty
Mary Hegarty University of California, Santa Barbara

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