World's Best Scientists 2026 revealed!
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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
122
Citations
101331
World Ranking
134
National Ranking
79

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2017 - ACM Fellow For contributions to machine learning with structured data, and innovations in scientific communication
  • 2009 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and application of information extraction, natural language processing, data mining, machine learning, and their integration.

Overview

Andrew McCallum is affiliated with the University of Massachusetts Amherst in the United States. Their research primarily focuses on computer science, with a substantial number of publications in artificial intelligence and related subfields. The main areas of study include artificial intelligence, computer vision and pattern recognition, molecular biology, signal processing, and management science and operations research.

The scientist has contributed extensively to topics such as topic modeling, natural language processing techniques, biomedical text mining and ontologies, multimodal machine learning applications, semantic web and ontologies, domain adaptation and few-shot learning, and advanced clustering algorithms research.

Key recent publications by Andrew McCallum include:

  • Energy and Policy Considerations for Modern Deep Learning Research, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Case-based Reasoning for Natural Language Queries over Knowledge Bases, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Unsupervised Pre-training for Biomedical Question Answering, 2020, arXiv (Cornell University)
  • Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Knowledge Base Question Answering by Case-based Reasoning over Subgraphs, 2022, arXiv (Cornell University)

The frequent co-authors collaborating with Andrew McCallum are Nicholas Monath, Michael Boratko, Manzil Zaheer, Shib Sankar Dasgupta, and Ameya Godbole. The scientist has published most notably in venues such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, and the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

Awards received by Andrew McCallum include being named an ACM Fellow in 2017 for contributions to machine learning with structured data and innovations in scientific communication. They are also a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), honored in 2009 for significant contributions to information extraction, natural language processing, data mining, machine learning, and their integration.

Best Publications

  • Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

    John D. Lafferty;Andrew McCallum;Fernando C. N. Pereira

  • A comparison of event models for naive bayes text classification

    Andrew McCallum;Kamal Nigam

  • Text Classification from Labeled and Unlabeled Documents using EM

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

  • Proceedings of the 25th international conference on Machine learning

    William Cohen;Andrew McCallum;Sam Roweis

  • Energy and Policy Considerations for Deep Learning in NLP

    Emma Strubell;Ananya Ganesh;Andrew McCallum

  • An Introduction to Conditional Random Fields for Relational Learning

    Charles Sutton;Andrew McCallum

  • Maximum Entropy Markov Models for Information Extraction and Segmentation

    Andrew McCallum;Dayne Freitag;Fernando C. N. Pereira

  • Introduction to Statistical Relational Learning

    Charles Sutton;Andrew McCallum

  • Topics over time: a non-Markov continuous-time model of topical trends

    Xuerui Wang;Andrew McCallum

  • Optimizing Semantic Coherence in Topic Models

    David Mimno;Hanna Wallach;Edmund Talley;Miriam Leenders

  • Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons

    Andrew McCallum;Wei Li

  • Efficient clustering of high-dimensional data sets with application to reference matching

    Andrew McCallum;Kamal Nigam;Lyle H. Ungar

  • Automating the Construction of Internet Portals with Machine Learning

    Andrew Kachites McCallum;Kamal Nigam;Jason Rennie;Kristie Seymore

  • Modeling relations and their mentions without labeled text

    Sebastian Riedel;Limin Yao;Andrew McCallum

  • Using Maximum Entropy for Text Classification

    Kamal Nigam;John Lafferty;Andrew McCallum

  • Toward Optimal Active Learning through Sampling Estimation of Error Reduction

    Nicholas Roy;Andrew McCallum

  • Employing EM and Pool-Based Active Learning for Text Classification

    Andrew McCallum;Kamal Nigam

  • Distributional clustering of words for text classification

    L. Douglas Baker;Andrew Kachites McCallum

  • Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

    Charles Sutton;Khashayar Rohanimanesh;Andrew McCallum

  • Learning to extract symbolic knowledge from the World Wide Web

    Mark Craven;Dan DiPasquo;Dayne Freitag;Andrew McCallum

  • An Introduction to Conditional Random Fields

    Charles Sutton;Andrew McCallum

  • Probabilistic Models for Segmenting and Labeling Sequence Data

    J. Lafferty;A. McCallum;F. Pereira;Kevin Duh

Frequent Co-Authors

Charles Sutton
Charles Sutton Google (United States)
Sebastian Riedel
Sebastian Riedel University College London
Sameer Singh
Sameer Singh University of California, Irvine
Aron Culotta
Aron Culotta Tulane University
David Mimno
David Mimno Cornell University
Chris Pal
Chris Pal Polytechnique Montréal
Fernando Pereira
Fernando Pereira Google (United States)
Hanna Wallach
Hanna Wallach Microsoft (United States)
Tom M. Mitchell
Tom M. Mitchell Carnegie Mellon University
Kevin Huang
Kevin Huang University of South Carolina

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