World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
78
Citations
28668
World Ranking
1186
National Ranking
631

Lise Getoor 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 Lise Getoor 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: 492 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Lise Getoor 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 Lise Getoor 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: 78 D-Index — 92nd percentile

92% 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

  • 2021 - IEEE Fellow For contributions to machine learning and reasoning under uncertainty
  • 2019 - ACM Fellow For contributions to machine learning, reasoning under uncertainty, and responsible data science
  • 2013 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to methods which combine probabilistic and logical representations in machine learning, knowledge discovery, graph mining, network analysis, and database systems.

Overview

Lise Getoor is affiliated with the University of California, Santa Cruz in the United States. Their research primarily falls under the broad field of Computer Science, with a focus on several subfields including Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Information Systems, and Management Science and Operations Research.

Their work spans multiple main topics of study, highlighting a diverse range of interests:

  • Topic Modeling
  • Bayesian Modeling and Causal Inference
  • Natural Language Processing Techniques
  • Complex Network Analysis Techniques
  • Explainable Artificial Intelligence (XAI)
  • Neural Networks and Applications
  • Speech and Dialogue Systems

Lise Getoor has contributed significantly to various academic venues, frequently publishing in:

  • arXiv (Cornell University)
  • Machine Learning
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the National Academy of Sciences
  • ACM Transactions on Interactive Intelligent Systems

Recent published papers demonstrate a focus on interdisciplinary approaches combining machine learning, causal inference, recommendation systems, and social media analysis. Notable recent works include:

  • Reducing opinion polarization: Effects of exposure to similar people with differing political views, 2021, Proceedings of the National Academy of Sciences
  • Generating and Understanding Personalized Explanations in Hybrid Recommender Systems, 2020, ACM Transactions on Interactive Intelligent Systems
  • ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation, 2023, arXiv (Cornell University)
  • Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • Causal Relational Learning, 2020, arXiv (Cornell University)

Their frequent co-authors indicate ongoing collaboration across multiple researchers, including:

  • Connor Pryor
  • Eriq Augustine
  • Alon Albalak
  • Sriram Srinivasan
  • Charles Dickens

Lise Getoor has received several professional recognitions including fellowships:

  • IEEE Fellow (2021) for contributions to machine learning and reasoning under uncertainty
  • ACM Fellow (2019) for contributions to machine learning, reasoning under uncertainty, and responsible data science
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) (2013) for significant contributions to methods combining probabilistic and logical representations in machine learning, knowledge discovery, graph mining, network analysis, and database systems

Best Publications

  • Collective Classification in Network Data

    Prithviraj Sen;Galileo Namata;Mustafa Bilgic;Lise Getoor

  • Introduction to statistical relational learning

    Lise Getoor;Ben Taskar

  • Link mining: a survey

    Lise Getoor;Christopher P. Diehl

  • Learning Probabilistic Relational Models

    Nir Friedman;Lise Getoor;Daphne Koller;Avi Pfeffer

  • Encyclopedia of Machine Learning and Data Mining

    Unknown

  • Collective entity resolution in relational data

    Indrajit Bhattacharya;Lise Getoor

  • Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

    Lise Getoor;Ben Taskar

  • To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

    Elena Zheleva;Lise Getoor

  • Link-based classification

    Qing Lu;Lise Getoor

  • Preserving the privacy of sensitive relationships in graph data

    Elena Zheleva;Lise Getoor

  • Entity resolution: theory, practice & open challenges

    Lise Getoor;Ashwin Machanavajjhala

  • Hinge-loss Markov random fields and probabilistic soft logic

    Stephen H. Bach;Matthias Broecheler;Bert Huang;Lise Getoor

  • Knowledge Graph Identification

    Jay Pujara;Hui Miao;Lise Getoor;William Cohen

  • A Latent Dirichlet Model for Unsupervised Entity Resolution

    Indrajit Bhattacharya;Lise Getoor

  • Selectivity estimation using probabilistic models

    Lise Getoor;Benjamin Taskar;Daphne Koller

  • Iterative record linkage for cleaning and integration

    Indrajit Bhattacharya;Lise Getoor

  • Method and apparatus for learning probabilistic relational models having attribute and link uncertainty and for performing selectivity estimation using probabilistic relational models

    Daphne Koller;Lise Getoor;Avi Pfeffer;Nir Friedman

  • 'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

    Naren Ramakrishnan;Patrick Butler;Sathappan Muthiah;Nathan Self

  • A short introduction to probabilistic soft logic

    Angelika Kimmig;Stephen Bach;Matthias Broecheler;Bert Huang

  • Query-driven active surveying for collective classification

    Lise Getoor

  • Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

    Lise Getoor;Ted Senator;Pedro Domingos;Christos Faloutsos

  • An Introduction to Conditional Random Fields for Relational Learning

    Lise Getoor;Ben Taskar

  • Collective Classification of Network Data.

    Ben London;Lise Getoor

  • Collective Classi!cation in Network Data

    Prithviraj Sen;Galileo Namata;Mustafa Bilgic;Lise Getoor

Frequent Co-Authors

Ben Taskar
Ben Taskar University of Washington
Amol Deshpande
Amol Deshpande University of Maryland, College Park
Kristina Lerman
Kristina Lerman University of Southern California
Daphne Koller
Daphne Koller insitro Inc.
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Stephen Muggleton
Stephen Muggleton Imperial College London
Naren Ramakrishnan
Naren Ramakrishnan Virginia Tech
Luc De Raedt
Luc De Raedt KU Leuven
Nir Friedman
Nir Friedman Weizmann Institute of Science
Hal Daumé
Hal Daumé University of Maryland, College Park

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