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D-Index & Metrics

Computer Science

D-Index
88
Citations
55819
World Ranking
659
National Ranking
350

Research.com Recognitions

  • 2020 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2010 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the field of machine learning and to the unification of first-order logic and probability.
  • 2003 - Fellow of Alfred P. Sloan Foundation

Overview

Pedro Domingos is affiliated with the University of Washington in the United States and has contributed extensively to several scientific fields, particularly at the intersection of computational and biological sciences.

Their research spans the primary fields of Biochemistry, Genetics and Molecular Biology, Medicine, and Computer Science. Within these broader fields, their work focuses on subfields including Molecular Biology, Cell Biology, Artificial Intelligence, Cellular and Molecular Neuroscience, and Oncology.

The main topics Pedro Domingos addresses in their research include:

  • Endoplasmic Reticulum Stress and Disease
  • Cellular transport and secretion
  • Parkinson's Disease Mechanisms and Treatments
  • Neurobiology and Insect Physiology Research
  • Bayesian Modeling and Causal Inference
  • RNA regulation and disease
  • HER2/EGFR in Cancer Research

Their recent publications demonstrate a blend of molecular biology and computational approaches, with papers such as:

  • "Genotoxic stress triggers the activation of IRE1α-dependent RNA decay to modulate the DNA damage response," 2020, Nature Communications
  • "Every Model Learned by Gradient Descent Is Approximately a Kernel Machine," 2020, arXiv (Cornell University)
  • "A Tractable First-Order Probabilistic Logic," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Pumilio protects Xbp1 mRNA from regulated Ire1-dependent decay," 2022, Nature Communications
  • "Genipin prevents alpha-synuclein aggregation and toxicity by affecting endocytosis, metabolism and lipid storage," 2023, Nature Communications

Pedro Domingos frequently publishes in venues such as:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Nature Communications
  • Journal of Visualized Experiments
  • Proceedings of the AAAI Conference on Artificial Intelligence

The researcher collaborates regularly with several co-authors, including:

  • Colin Adrain
  • Catarina J Gaspar
  • Gonçalo M. Poças
  • Cristiana C Santos
  • Marina Badenes

Pedro Domingos has been recognized as a Fellow of multiple professional associations. These include the American Association for the Advancement of Science (AAAS) in 2020, the Association for the Advancement of Artificial Intelligence (AAAI) in 2010 for contributions to machine learning and the unification of first-order logic and probability, and the Alfred P. Sloan Foundation in 2003.

Best Publications

  • On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

    Pedro Domingos;Michael Pazzani

  • A few useful things to know about machine learning

    Pedro Domingos

  • Markov logic networks

    Matthew Richardson;Pedro Domingos

  • Mining the network value of customers

    Pedro Domingos;Matt Richardson

  • Mining high-speed data streams

    Pedro Domingos;Geoff Hulten

  • Mining time-changing data streams

    Geoff Hulten;Laurie Spencer;Pedro Domingos

  • Mining knowledge-sharing sites for viral marketing

    Matthew Richardson;Pedro Domingos

  • MetaCost: a general method for making classifiers cost-sensitive

    Pedro Domingos

  • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

    Pedro Domingos

  • Learning to map between ontologies on the semantic web

    AnHai Doan;Jayant Madhavan;Pedro Domingos;Alon Halevy

  • Trust management for the semantic web

    Matthew Richardson;Rakesh Agrawal;Pedro Domingos

  • Adversarial classification

    Nilesh Dalvi;Pedro Domingos;Sumit Sanghai

  • Reconciling schemas of disparate data sources: a machine-learning approach

    AnHai Doan;Pedro Domingos;Alon Y. Halevy

  • Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier.

    Pedro M. Domingos;Michael J. Pazzani

  • Sum-product networks: a new deep architecture

    Hoifung Poon;Pedro Domingos

  • Ontology Matching: A Machine Learning Approach

    AnHai Doan;Jayant Madhavan;Pedro M. Domingos;Alon Y. Halevy

  • Learning to match ontologies on the Semantic Web

    AnHai Doan;Jayant Madhavan;Robin Dhamankar;Pedro Domingos

  • Tree Induction for Probability-Based Ranking

    Foster Provost;Pedro Domingos

  • The Role of Occam‘s Razor in Knowledge Discovery

    Pedro Domingos

  • The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank

    Matthew Richardson;Pedro Domingos

  • Markov Logic: An Interface Layer for Artificial Intelligence

    Pedro Domingos;Daniel Lowd

Frequent Co-Authors

Hoifung Poon
Hoifung Poon Microsoft (United States)
Matthew Richardson
Matthew Richardson Microsoft (United States)
Daniel S. Weld
Daniel S. Weld University of Washington
AnHai Doan
AnHai Doan University of Wisconsin–Madison
Alon Halevy
Alon Halevy Facebook (United States)
Jayant Madhavan
Jayant Madhavan Google (United States)
Michael J. Pazzani
Michael J. Pazzani University of California, Riverside
Raymond J. Mooney
Raymond J. Mooney The University of Texas at Austin
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Oren Etzioni
Oren Etzioni University of Washington

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