World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
60
Citations
16981
World Ranking
3201
National Ranking
1553

Overview

Geoffrey J. Gordon is affiliated with Carnegie Mellon University in the United States, focusing his research primarily within the field of Computer Science. His work spans several key subfields including Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Electrical and Electronic Engineering, and Safety Research.

The scientist's studies cover a variety of important topics such as Explainable Artificial Intelligence (XAI), Advanced Graph Neural Networks, Reinforcement Learning in Robotics, Advanced Bandit Algorithms Research, Distributed Systems and Fault Tolerance, Distributed and Parallel Computing Systems, and Green IT and Sustainability.

Throughout their career, Geoffrey J. Gordon has contributed to research published in multiple venues. Frequent publication locations include:

  • arXiv (Cornell University)
  • IEEE Internet Computing
  • Proceedings of the AAAI Conference on Artificial Intelligence

Their recent papers illustrate active involvement in advancing knowledge within these fields. Notable recent publications include:

  • Information Obfuscation of Graph Neural Networks, 2020, arXiv (Cornell University)
  • Understanding and Mitigating Accuracy Disparity in Regression, 2021, arXiv (Cornell University)
  • A Vision for Computational Decarbonization of Societal Infrastructure, 2025, IEEE Internet Computing
  • Learning General Latent-Variable Graphical Models with Predictive Belief Propagation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Successor Feature Sets: Generalizing Successor Representations Across Policies, 2021, arXiv (Cornell University)

Collaborative work is an element of their research process, with frequent co-authors including Han Zhao, Kianté Brantley, Soroush Mehri, David Irwin, and Prashant Shenoy.

Best Publications

  • A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

    Stéphane Ross;Geoffrey J. Gordon;J. Andrew Bagnell

  • Relational learning via collective matrix factorization

    Ajit P. Singh;Geoffrey J. Gordon

  • ARA*: Anytime A* with Provable Bounds on Sub-Optimality

    Maxim Likhachev;Geoffrey J. Gordon;Sebastian Thrun

  • Stable function approximation in dynamic programming

    Geoffrey J. Gordon

  • Automatic Database Management System Tuning Through Large-scale Machine Learning

    Dana Van Aken;Andrew Pavlo;Geoffrey J. Gordon;Bohan Zhang

  • Individualized Bayesian Knowledge Tracing Models

    Michael V. Yudelson;Kenneth R. Koedinger;Geoffrey J. Gordon

  • Brief paper: Decentralized estimation and control of graph connectivity for mobile sensor networks

    P. Yang;R. A. Freeman;G. J. Gordon;K. M. Lynch

  • Anytime point-based approximations for large POMDPs

    Joelle Pineau;Geoffrey Gordon;Sebastian Thrun

  • Finding approximate POMDP solutions through belief compression

    Nicholas Roy;Geoffrey Gordon;Sebastian Thrun

  • Adversarial Multiple Source Domain Adaptation

    Han Zhao;Shanghang Zhang;Guanhang Wu;José M. F. Moura

  • Anytime search in dynamic graphs

    Maxim Likhachev;Dave Ferguson;Geoff Gordon;Anthony Stentz

  • Planning in the presence of cost functions controlled by an adversary

    H. Brendan McMahan;Geoffrey J. Gordon;Avrim Blum

  • An Empirical Study of Example Forgetting during Deep Neural Network Learning

    Mariya Toneva;Alessandro Sordoni;Remi Tachet des Combes;Adam Trischler

  • On learning invariant representations for domain adaptation

    Han Zhao;Remi Tachet des Combes;Kun Zhang;Geoffrey J. Gordon;Geoffrey J. Gordon

  • An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality

    Gregory F. Cooper;Constantin F. Aliferis;Richard Ambrosino;John M. Aronis

  • Real-time fault diagnosis [robot fault diagnosis]

    V. Verma;G. Gordon;R. Simmons;S. Thrun

  • Closing the learning-planning loop with predictive state representations

    Byron Boots;Sajid M Siddiqi;Geoffrey J Gordon

  • A Unified View of Matrix Factorization Models

    Ajit P. Singh;Geoffrey J. Gordon

  • Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs

    Rosemary Emery-Montemerlo;Geoff Gordon;Jeff Schneider;Sebastian Thrun

  • Hilbert Space Embeddings of Hidden Markov Models

    Le Song;Byron Boots;Sajid M. Siddiqi;Geoffrey J. Gordon

Frequent Co-Authors

Byron Boots
Byron Boots University of Washington
Sebastian Thrun
Sebastian Thrun Stanford University
Siddhartha S. Srinivasa
Siddhartha S. Srinivasa University of Washington
J. Andrew Bagnell
J. Andrew Bagnell Carnegie Mellon University
André Platzer
André Platzer Karlsruhe Institute of Technology
Joelle Pineau
Joelle Pineau McGill University
H. Brendan McMahan
H. Brendan McMahan Google (United States)
Miroslav Dudík
Miroslav Dudík Microsoft (United States)
Maxim Likhachev
Maxim Likhachev Carnegie Mellon University
Robert F. Murphy
Robert F. Murphy Carnegie Mellon University

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