H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 49 Citations 70,400 177 World Ranking 2989 National Ranking 61

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

Gaël Varoquaux mainly focuses on Artificial intelligence, Machine learning, Python, Neuroimaging and Resting state fMRI. His research integrates issues of Functional magnetic resonance imaging, Functional neuroimaging and Pattern recognition in his study of Artificial intelligence. His Machine learning research is multidisciplinary, relying on both Generalizability theory, Functional connectivity and Source code.

Gaël Varoquaux has researched Python in several fields, including Computation, Documentation and Computational science. His Neuroimaging research integrates issues from Decoding methods, Cross-validation and Model selection. His studies in Resting state fMRI integrate themes in fields like Segmentation, Independent component analysis, Atlas, Brain activity and meditation and Subject.

His most cited work include:

  • Scikit-learn: Machine Learning in Python (25406 citations)
  • The NumPy Array: A Structure for Efficient Numerical Computation (6222 citations)
  • Machine learning for neuroimaging with scikit-learn. (634 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Neuroimaging and Functional magnetic resonance imaging. His study in Voxel and Supervised learning falls under the purview of Artificial intelligence. His Machine learning research is multidisciplinary, relying on both Decoding methods, Inference and Functional connectivity.

His study in the field of Independent component analysis, Segmentation and Dimensionality reduction is also linked to topics like Context. The concepts of his Neuroimaging study are interwoven with issues in Cognition and Data mining. The Data mining study combines topics in areas such as Scalability and Outlier.

He most often published in these fields:

  • Artificial intelligence (58.18%)
  • Machine learning (31.64%)
  • Pattern recognition (31.64%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (58.18%)
  • Neuroimaging (23.27%)
  • Cognition (12.73%)

In recent papers he was focusing on the following fields of study:

His main research concerns Artificial intelligence, Neuroimaging, Cognition, Pattern recognition and Machine learning. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Estimator and Functional data analysis. As a member of one scientific family, he mostly works in the field of Neuroimaging, focusing on Decoding methods and, on occasion, Statistical inference, Empirical research and Multivariate statistics.

His Cognition research incorporates elements of Functional magnetic resonance imaging, Cognitive science and Inference. His Hyperparameter study in the realm of Machine learning interacts with subjects such as Benchmarking. His research in Benchmark tackles topics such as Supervised learning which are related to areas like Schizophrenia.

Between 2018 and 2021, his most popular works were:

  • Establishment of Best Practices for Evidence for Prediction: A Review. (93 citations)
  • Benchmarking functional connectome-based predictive models for resting-state fMRI. (85 citations)
  • International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium (32 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

His primary scientific interests are in Artificial intelligence, Pattern recognition, Cognition, Neuroimaging and Observational study. His Artificial intelligence study frequently links to related topics such as Machine learning. His work in Machine learning addresses issues such as Benchmark, which are connected to fields such as Schizophrenia.

His Pattern recognition research includes themes of Data compression, Electroencephalography, Magnetoencephalography and Functional data analysis. His research integrates issues of Mental health, Cognitive science and Inference in his study of Cognition. His Observational study study integrates concerns from other disciplines, such as Psychological intervention, Test data generation and Epidemiology.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Scikit-learn: Machine Learning in Python

Fabian Pedregosa;Gaël Varoquaux;Alexandre Gramfort;Vincent Michel.
Journal of Machine Learning Research (2011)

40587 Citations

The NumPy Array: A Structure for Efficient Numerical Computation

Stéfan van der Walt;S Chris Colbert;Gaël Varoquaux.
Computing in Science and Engineering (2011)

7429 Citations

Scikit-learn: Machine Learning in Python

Fabian Pedregosa;Gaël Varoquaux;Alexandre Gramfort;Vincent Michel.
arXiv: Learning (2012)

5180 Citations

API design for machine learning software: experiences from the scikit-learn project

Lars Buitinck;Gilles Louppe;Mathieu Blondel;Fabian Pedregosa.
european conference on machine learning (2013)

859 Citations

Machine learning for neuroimaging with scikit-learn.

Alexandre Abraham;Alexandre Abraham;Fabian Pedregosa;Fabian Pedregosa;Michael Eickenberg;Michael Eickenberg;Philippe Gervais;Philippe Gervais.
Frontiers in Neuroinformatics (2014)

741 Citations

Mayavi: 3D Visualization of Scientific Data

P Ramachandran;G Varoquaux.
computational science and engineering (2011)

564 Citations

Mayavi: a package for 3D visualization of scientific data

Prabhu Ramachandran;Gaël Varoquaux.
arXiv: Software Engineering (2010)

494 Citations

NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

Krzysztof J. Gorgolewski;Krzysztof J. Gorgolewski;Gael Varoquaux;Gabriel Rivera;Yannick Schwarz.
Frontiers in Neuroinformatics (2015)

393 Citations

Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

Gaël Varoquaux;Pradeep Reddy Raamana;Denis A. Engemann;Andrés Hoyos-Idrobo.
NeuroImage (2017)

338 Citations

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Krzysztof J. Gorgolewski;Tibor Auer;Vince D. Calhoun;R. Cameron Craddock.
Scientific Data (2016)

337 Citations

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Simon B. Eickhoff

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Vince D. Calhoun

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Michael P. Milham

University of California, Davis

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Alexandre Gramfort

French Institute for Research in Computer Science and Automation - INRIA

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Russell A. Poldrack

Stanford University

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Christian F. Beckmann

Christian F. Beckmann

Radboud University Nijmegen

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Stephen M. Smith

Stephen M. Smith

University of Oxford

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Jason H. Moore

Jason H. Moore

University of Pennsylvania

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B.T. Thomas Yeo

B.T. Thomas Yeo

National University of Singapore

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Thomas E. Nichols

Thomas E. Nichols

University of Oxford

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Daniel S. Margulies

Centre national de la recherche scientifique, CNRS

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Bernhard O. Palsson

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Theodore D. Satterthwaite

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