World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
9098
World Ranking
11913
National Ranking
298

Overview

Jesse Read is affiliated with École Polytechnique in France and has a research focus primarily within Computer Science, with 93 publications in the field. Their work extensively covers subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Molecular Biology, and Electrical and Electronic Engineering.

The scientist's research topics include a range of areas within machine learning and data analysis. Key topics addressed by Jesse Read are:

  • Data Stream Mining Techniques
  • Machine Learning and Data Classification
  • Text and Document Classification Technologies
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Algorithms
  • Time Series Analysis and Forecasting
  • Explainable Artificial Intelligence (XAI)

Among recent publications, Jesse Read is the lead author of the paper titled "MEKA: A multi-label/multi-target extension to Weka", published in 2025 at Aaltodoc (Aalto University). This paper has accrued 217 citations. Another notable work includes book publications through Springer Science+Business Media, predominantly under the series titled Machine Learning and Knowledge Discovery in Databases. Research Track, all published in 2021.

Frequent coauthors collaborating with Jesse Read include Rim Kaddah, Albert Bifet, Luca Martino, Indrė Žliobaitė, and Heitor Murilo Gomes. The scientist has contributed to publication venues such as:

  • arXiv (Cornell University)
  • ACM Computing Surveys
  • Machine Learning
  • IEEE Transactions on Biomedical Engineering
  • Knowledge and Information Systems

Recent papers by or related to Jesse Read's research circle include:

  • MEKA: A multi-label/multi-target extension to Weka, 2025, Aaltodoc (Aalto University)
  • River: machine learning for streaming data in Python, 2020, arXiv (Cornell University)
  • A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers, 2020, arXiv (Cornell University)
  • A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams, 2022, ACM Computing Surveys
  • On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG, 2023, IEEE Transactions on Biomedical Engineering

Best Publications

  • Classifier chains for multi-label classification

    Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank

  • Classifier Chains for Multi-label Classification

    Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank

  • Adaptive random forests for evolving data stream classification

    Heitor M. Gomes;Albert Bifet;Jesse Read;Jean Paul Barddal

  • Multi-label Classification Using Ensembles of Pruned Sets

    J. Read;B. Pfahringer;G. Holmes

  • Scikit-Multiflow: A Multi-output Streaming Framework

    Jacob Montiel;Jesse Read;Albert Bifet;Talel Abdessalem

  • Meka: a multi-label/multi-target extension to weka

    Jesse Read;Peter Reutemann;Bernhard Pfahringer;Geoff Holmes

  • A Pruned Problem Transformation Method for Multi-label Classification

    Jesse Read

  • Machine learning for streaming data: state of the art, challenges, and opportunities

    Heitor Murilo Gomes;Jesse Read;Albert Bifet;Jean Paul Barddal

  • Efficient Online Evaluation of Big Data Stream Classifiers

    Albert Bifet;Gianmarco de Francisci Morales;Jesse Read;Geoff Holmes

  • Scalable and efficient multi-label classification for evolving data streams

    Jesse Read;Albert Bifet;Geoff Holmes;Bernhard Pfahringer

  • Batch-incremental versus instance-incremental learning in dynamic and evolving data

    Jesse Read;Albert Bifet;Bernhard Pfahringer;Geoff Holmes

  • River: Machine learning for streaming data in Python

    Jacob Montiel;Max Halford;Saulo Martiello Mastelini;Geoffrey Bolmier

  • Cooperative parallel particle filters for online model selection and applications to urban mobility

    Luca Martino;Jesse Read;Jesse Read;Víctor Elvira;Francisco Louzada

  • Efficient monte carlo methods for multi-dimensional learning with classifier chains

    Jesse Read;Luca Martino;David Luengo

  • Evaluation methods and decision theory for classification of streaming data with temporal dependence

    Indrăź źLiobaităź;Albert Bifet;Jesse Read;Bernhard Pfahringer

  • Better Sign Language Translation with STMC-Transformer

    Kayo Yin;Jesse Read

  • Scalable Multi-label Classification

    Jesse Read

  • Streaming Random Patches for Evolving Data Stream Classification

    Heitor Murilo Gomes;Jesse Read;Albert Bifet

  • Pitfalls in benchmarking data stream classification and how to avoid them

    Albert Bifet;Jesse Read;Indrė Žliobaitė;Bernhard Pfahringer

  • Independent Doubly Adaptive Rejection Metropolis Sampling Within Gibbs Sampling

    Luca Martino;Jesse Read;David Luengo

  • Efficient data stream classification via probabilistic adaptive windows

    Albert Bifet;Bernhard Pfahringer;Jesse Read;Geoff Holmes

Frequent Co-Authors

Albert Bifet
Albert Bifet University of Waikato
Luca Martino
Luca Martino King Juan Carlos University
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Michalis Vazirgiannis
Michalis Vazirgiannis École Polytechnique
Eibe Frank
Eibe Frank University of Waikato
Geoffrey Holmes
Geoffrey Holmes University of Waikato
Thomas Seidl
Thomas Seidl Ludwig-Maximilians-Universität München
Eduard Ayguadé
Eduard Ayguadé Barcelona Supercomputing Center
Harri Mäkinen
Harri Mäkinen Natural Resources Institute Finland

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