World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

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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