World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
7227
World Ranking
8838
National Ranking
3773

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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