World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
46543
World Ranking
9919
National Ranking
4165

Overview

George E. Dahl is affiliated with Google in the United States, where their research primarily spans the field of Computer Science with a focus on Artificial Intelligence. Their work involves various subfields including Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics, and Computational Theory and Mathematics.

The scientist's main research topics include:

  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Advanced Graph Neural Networks
  • Artificial Intelligence in Healthcare and Education
  • Gaussian Processes and Bayesian Inference

George E. Dahl has contributed significantly to several publication venues, with the majority of their work appearing in:

  • arXiv (Cornell University)
  • Nature Communications
  • Communications Medicine

Some of their recent papers include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems, 2025, arXiv (Cornell University)
  • Machine learning guided aptamer refinement and discovery, 2021, Nature Communications
  • A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment, 2022, Communications Medicine
  • Pre-trained Gaussian Processes for Bayesian Optimization, 2021, arXiv (Cornell University)
  • A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes, 2021, arXiv (Cornell University)

Collaborations form a notable part of their research, with frequent coauthors including:

  • Justin Gilmer
  • Zachary Nado
  • Sourabh Medapati
  • Jasper Snoek
  • Rohan Anil

Best Publications

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

    G. Hinton;Li Deng;Dong Yu;G. E. Dahl

  • On the importance of initialization and momentum in deep learning

    Ilya Sutskever;James Martens;George Dahl;Geoffrey Hinton

  • Neural Message Passing for Quantum Chemistry

    Justin Gilmer;Samuel S. Schoenholz;Patrick F. Riley;Oriol Vinyals

  • Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

    G. E. Dahl;Dong Yu;Li Deng;A. Acero

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition

    Geoffrey Hinton;Li Deng;Dong Yu;George Dahl

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Acoustic Modeling Using Deep Belief Networks

    A. Mohamed;G. E. Dahl;G. Hinton

  • Improving deep neural networks for LVCSR using rectified linear units and dropout

    George E. Dahl;Tara N. Sainath;Geoffrey E. Hinton

  • Deep Convolutional Neural Networks for Large-scale Speech Tasks

    Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau

  • Deep neural nets as a method for quantitative structure-activity relationships.

    Junshui Ma;Robert P. Sheridan;Andy Liaw;George E. Dahl

  • Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

    Felix A. Faber;Luke Hutchison;Bing Huang;Justin Gilmer

  • Large-scale malware classification using random projections and neural networks

    George E. Dahl;Jack W. Stokes;Li Deng;Dong Yu

  • Detecting Cancer Metastases on Gigapixel Pathology Images

    Yun Liu;Krishna Kumar Gadepalli;Mohammad Norouzi;George Dahl

  • Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

    George Dahl;Marc'aurelio Ranzato;Abdel-rahman Mohamed;Geoffrey E. Hinton

  • Deep Belief Networks using discriminative features for phone recognition

    Abdel-rahman Mohamed;Tara N. Sainath;George Dahl;Bhuvana Ramabhadran

  • Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists.

    Yun Liu;Timo Kohlberger;Mohammad Norouzi;George E. Dahl

  • Large scale distributed neural network training through online distillation

    Rohan Anil;Gabriel Pereyra;Alexandre Tachard Passos;Robert Ormandi

  • Improvements to Deep Convolutional Neural Networks for LVCSR

    Tara N. Sainath;Brian Kingsbury;Abdel-rahman Mohamed;George E. Dahl

  • Multi-task Neural Networks for QSAR Predictions

    George E. Dahl;Navdeep Jaitly;Ruslan Salakhutdinov

  • Measuring the Effects of Data Parallelism on Neural Network Training

    Christopher J. Shallue;Jaehoon Lee;Joseph M. Antognini;Jascha Sohl-Dickstein

  • Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

    Ryan Prescott Adams;George E. Dahl;Iain Murray

  • The shared views of four research groups )

    Geoffrey Hinton;Li Deng;Dong Yu;George E. Dahl

Frequent Co-Authors

Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)
Li Deng
Li Deng Citadel
Dong Yu
Dong Yu Tencent (China)
Tara N. Sainath
Tara N. Sainath Google (United States)
Ryan P. Adams
Ryan P. Adams Princeton University
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Brian Kingsbury
Brian Kingsbury IBM (United States)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Computer Science isn't the only tech-driven path available for students interested in problem-solving and innovation. There are several related online degrees that connect to promising, high-impact careers. For example, engineering fields like online mechanical engineering degree programs offer skills in automation, robotics, and product design, which often overlap with computer science roles.

Online education is also making it easier to gain credentials quickly and affordably. If you’re eager to enter the workforce, accelerated cs degree options can help you earn your qualifications faster. Additionally, students passionate about sustainability may want to explore programs in environmental science. Want to know where this path can take you? Read about what can you do with an environmental studies degree for more ideas.

For those who want an interdisciplinary approach, consider an environmental engineering degrees online to blend technology, sustainability, and engineering principles. Each of these online degrees opens doors to in-demand, rewarding career pathways both in the US and globally.

Best Scientists Citing George E. Dahl

Trending Scientists

Recently Published Articles