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

D-Index & Metrics

Computer Science

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

Andrew McCallum publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Andrew McCallum sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 442 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Andrew McCallum D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Andrew McCallum sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 122 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Choosing to study Computer Science in the USA opens up a range of flexible online degree options and career directions. For students who want to fast-track their education, a computer science accelerated program can help you earn your degree quicker and start your career sooner.

Careers in engineering are another popular option for those with a background in computer science. If sustainability is your passion, an environmental engineer degree online can lead to roles focusing on eco-friendly technology and innovation. For those interested in core engineering skills, you can save on tuition costs by enrolling in the cheapest online mechanical engineering degree programs.

Furthermore, science enthusiasts might consider an online physics bachelor's degree—perfect for developing quantitative and analytical skills valued across tech industries.

These related degrees can all be accessed online, making it easier to balance studies with work or other responsibilities while pursuing rewarding technology and engineering careers.

Best Scientists Citing Andrew McCallum

Trending Scientists