World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
92
Citations
69087
World Ranking
534
National Ranking
285

Overview

Tom M. Mitchell is affiliated with Carnegie Mellon University in the United States. Their research primarily falls within the field of Computer Science, with a focus on Artificial Intelligence, Cognitive Neuroscience, and Developmental and Educational Psychology.

Mitchell's work covers various subfields including Computer Science Applications and Communication. Their research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Intelligent Tutoring Systems and Adaptive Learning
  • Neurobiology of Language and Bilingualism
  • Online Learning and Analytics
  • Reading and Literacy Development

The researcher has published extensively, contributing at least 71 works in Computer Science. A significant portion of their publications appears in venues such as arXiv (Cornell University) with 21 papers, bioRxiv (Cold Spring Harbor Laboratory) with 3 papers, Zenodo (CERN European Organization for Nuclear Research) with 2 papers, Proceedings of the AAAI Conference on Artificial Intelligence with 2 papers, and Patterns with 2 papers.

Some recent papers authored or co-authored by Tom M. Mitchell include:

  • "What skills and abilities can automation technologies replicate and what does it mean for workers?" (2022), published in OECD social employment and migration working papers
  • "When is Deep Learning the Best Approach to Knowledge Tracing?" (2020), published in Zenodo (CERN European Organization for Nuclear Research)
  • "Protecting scientific integrity in an age of generative AI" (2024), published in Proceedings of the National Academy of Sciences
  • "Combining computational controls with natural text reveals aspects of meaning composition" (2022), published in Nature Computational Science
  • "Combining computational controls with natural text reveals new aspects of meaning composition" (2020), published in bioRxiv (Cold Spring Harbor Laboratory)

Throughout their career, Mitchell has collaborated frequently with several co-authors. These include Ashiqur R. KhudaBukhsh, Amos Azaria, Mary L. Gray, Robin Schmucker, and Forough Arabshahi, each contributing to multiple joint publications.

Best Publications

  • Machine learning: Trends, perspectives, and prospects

    M. I. Jordan;T. M. Mitchell

  • Combining labeled and unlabeled data with co-training

    Avrim Blum;Tom Mitchell

  • Text Classification from Labeled and Unlabeled Documents using EM

    Kamal Nigam;Andrew Kachites McCallum;Sebastian Thrun;Tom Mitchell

  • Machine Learning: An Artificial Intelligence Approach

    R. S. Michalski;J. G. Carbonell;T. M. Mitchell

  • Toward an architecture for never-ending language learning

    Andrew Carlson;Justin Betteridge;Bryan Kisiel;Burr Settles

  • Generalization as search

    Tom M. Mitchell

  • Explanation-based generalization: a unifying view

    Tom M. Mitchell;Richard M. Keller;Smadar T. Kedar-Cabelli

  • Machine learning classifiers and fMRI: a tutorial overview

    Francisco Pereira;Tom M. Mitchell;Matthew Botvinick

  • Predicting Human Brain Activity Associated with the Meanings of Nouns

    Tom M. Mitchell;Svetlana V. Shinkareva;Andrew Carlson;Kai-Min Chang

  • Zero-shot Learning with Semantic Output Codes

    Mark Palatucci;Dean Pomerleau;Geoffrey E. Hinton;Tom M. Mitchell

  • Web Watcher: A Tour Guide for the World Wide Web.

    Thorsten Joachims;Dayne Freitag;Tom M. Mitchell

  • Never-ending learning

    T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar

  • The Need for Biases in Learning Generalizations

    Tom M. Mitchell

  • Learning to extract symbolic knowledge from the World Wide Web

    Mark Craven;Dan DiPasquo;Dayne Freitag;Andrew McCallum

  • What can machine learning do? Workforce implications.

    Erik Brynjolfsson;Erik Brynjolfsson;Tom Mitchell

  • WebWatcher : A Learning Apprentice for the World Wide Web

    Robert Armstrong;Dayne Freitag;Thorsten Joachims;Tom Mitchell

  • Learning to Decode Cognitive States from Brain Images

    Tom M. Mitchell;Rebecca Hutchinson;Radu S. Niculescu;Francisco Pereira

  • Machine learning and data mining

    Tom M. Mitchell

  • Never-ending learning

    T. Mitchell;W. Cohen;E. Hruschka;P. Talukdar

  • LEAP: a learning apprentice for VLSI design

    Tom M. Mitchell;Sridbar Mahadevan;Louis I. Steinberg

Frequent Co-Authors

Partha Pratim Talukdar
Partha Pratim Talukdar Indian Institute of Science
Marcel Adam Just
Marcel Adam Just Carnegie Mellon University
Sebastian Thrun
Sebastian Thrun Stanford University
Christos Faloutsos
Christos Faloutsos Carnegie Mellon University
Jaime G. Carbonell
Jaime G. Carbonell Carnegie Mellon University
Ryszard S. Michalski
Ryszard S. Michalski George Mason University
Matt Gardner
Matt Gardner Allen Institute for Artificial Intelligence
Evangelos E. Papalexakis
Evangelos E. Papalexakis University of California, Riverside
Nicholas D. Sidiropoulos
Nicholas D. Sidiropoulos University of Virginia
Bruce G. Buchanan
Bruce G. Buchanan University of Pittsburgh

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