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Computer Science

D-Index
93
Citations
85039
World Ranking
498
National Ranking
266

Overview

John Platt is a researcher affiliated with Google in the United States, whose work spans multiple areas at the intersection of engineering and environmental science. Their primary research contributions focus largely on engineering disciplines, with a notable emphasis on aerospace engineering and electrical and electronic engineering.

Their research also covers environmental science fields including environmental engineering and topics related to health, toxicology, and mutagenesis. This interdisciplinary approach is reflected in their engagement with subjects such as air quality and health impacts, air quality monitoring and forecasting, as well as traffic prediction and management techniques.

John Platt is involved in research themes that include energy load and power forecasting, magnetic confinement fusion research, the aviation industry's analysis and trends, and advanced aircraft design and technologies. These multiple topics demonstrate a broad spectrum of interests tied to the integration of engineering principles with environmental and energy challenges.

Their recent publications include papers in various respected venues, reflecting collaborations and contributions in diverse domains. Notable recent papers include:

  • "Tackling Climate Change with Machine Learning," 2022, OPUS 4 (Zuse Institute Berlin)
  • "Tackling Climate Change with Machine Learning," 2022, ACM Computing Surveys
  • "CO2 capture by pumping surface acidity to the deep ocean," 2022, Energy & Environmental Science
  • "A scalable system to measure contrail formation on a per-flight basis," 2023, Environmental Research Communications
  • "Enhanced plasma performance in C-2W advanced beam-driven field-reversed configuration experiments," 2024, Nuclear Fusion

These works cover a diverse range of subjects from climate change mitigation using machine learning to advancements in plasma performance for fusion research, aligning with the scientist's main areas of study.

John Platt frequently collaborates with several coauthors, including Erica Brand, Scott Geraedts, Zebediah Engberg, Kevin McCloskey, and Tharun Sankar. These collaborations have contributed to a consistent output of research articles across prominent publication venues.

The venues where John Platt often publishes include arXiv (Cornell University), Environmental Research Communications, OPUS 4 (Zuse Institute Berlin), ACM Computing Surveys, and Energy & Environmental Science. These outlets represent a mix of open-access repositories and peer-reviewed journals covering environmental and engineering research.

Best Publications

  • Fast training of support vector machines using sequential minimal optimization

    John C. Platt

  • Quantum supremacy using a programmable superconducting processor

    Frank Arute;Kunal Arya;Ryan Babbush;Dave Bacon

  • Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods

    John C. Platt

  • Estimating the Support of a High-Dimensional Distribution

    Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola

  • Support vector machines

    M.A. Hearst;S.T. Dumais;E. Osman;J. Platt

  • Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines

    John C. Platt

  • Best practices for convolutional neural networks applied to visual document analysis

    P.Y. Simard;D. Steinkraus;J.C. Platt

  • Supplementary information for "Quantum supremacy using a programmable superconducting processor"

    Frank Arute;Kunal Arya;Ryan Babbush;Dave Bacon

  • Elastically deformable models

    Demetri Terzopoulos;John Platt;Alan Barr;Kurt Fleischer

  • Large Margin DAGs for Multiclass Classification

    John C. Platt;Nello Cristianini;John Shawe-Taylor

  • Support Vector Method for Novelty Detection

    Bernhard Schölkopf;Robert C Williamson;Alex J. Smola;John Shawe-Taylor

  • Inductive learning algorithms and representations for text categorization

    Susan Dumais;John Platt;David Heckerman;Mehran Sahami

  • A resource-allocating network for function interpolation

    John Platt

  • From captions to visual concepts and back

    Hao Fang;Saurabh Gupta;Forrest Iandola;Rupesh K. Srivastava

  • A technique which utilizes a probabilistic classifier to detect "junk" e-mail

    Eric Horvitz;David E. Heckerman;Susan T. Dumais;Mehran Sahami

  • Multiple Instance Boosting for Object Detection

    Cha Zhang;John C. Platt;Paul A. Viola

  • Auto playlist generation with multiple seed songs

    John C. Platt

  • Using Analytic QP and Sparseness to Speed Training of Support Vector Machines

    John C. Platt

  • Constraints methods for flexible models

    John C. Platt;Alan H. Barr

  • Hidden conditional random fields for phone classification.

    Asela Gunawardana;Milind Mahajan;Alex Acero;John C. Platt

  • Large Margin DAG's for Multiclass Classification

    John Platt;Nello Cristianini;John Shawe-Taylor

  • Greedy Layer-Wise Training of Deep Networks

    Bernhard Schölkopf;John Platt;Thomas Hofmann

  • WILLIAMSON, ESTIMATING THE SUPPORT OF A HIGH-DIMENSIONAL DISTRIBUTION

    B Scholkopf;J C Platt;J Shawe Taylor

  • Analysis of Representations for Domain Adaptation

    Bernhard Schölkopf;John Platt;Thomas Hofmann

Frequent Co-Authors

Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Thomas Hofmann
Thomas Hofmann ETH Zurich
Christopher J. C. Burges
Christopher J. C. Burges Microsoft (United States)
Eric Horvitz
Eric Horvitz Microsoft (United States)
Gary W. Flake
Gary W. Flake Independent Scientist / Consultant, US
Jonathan Goldstein
Jonathan Goldstein Microsoft (United States)
Robert L. Rounthwaite
Robert L. Rounthwaite Microsoft (United States)
William H. Gates
William H. Gates Microsoft (United States)
Li Deng
Li Deng Citadel

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