World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
13540
World Ranking
4513
National Ranking
2115

Ani Nenkova 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 Ani Nenkova 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: 139 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Ani Nenkova 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 Ani Nenkova 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Ani Nenkova is affiliated with Adobe Systems in the United States. Their research spans a range of topics primarily focused on Artificial Intelligence and related fields. The subfields of study in which they have contributed include Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Management Science and Operations Research, and Statistics, Probability and Uncertainty.

Their work often intersects with multiple areas of advanced data processing and analysis, especially in Natural Language Processing (NLP) and biomedical applications. Key topics addressed in their research include Topic Modeling, Natural Language Processing Techniques, Biomedical Text Mining and Ontologies, Data Quality and Management, Advanced Text Analysis Techniques, Meta-analysis and systematic reviews, and Handwritten Text Recognition Techniques.

Recent publications by Ani Nenkova reflect a focus on medical informatics, language processing, and named entity recognition. Among these are:

  • Trialstreamer: A living, automatically updated database of clinical trial reports (2020), published in the Journal of the American Medical Informatics Association
  • Temporal Effects on Pre-trained Models for Language Processing Tasks (2022), featured in Transactions of the Association for Computational Linguistics
  • Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time (2020), available on PubMed
  • Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models (2020), published on arXiv (Cornell University)
  • State of the evidence: a survey of global disparities in clinical trials (2021), published in BMJ Global Health

Nenkova frequently collaborates with several researchers, including Iain Marshall, Byron Wallace, Benjamin Nye, Frank Soboczenski, and James Thomas. These coauthors have worked with Nenkova on numerous projects across various studies.

The scientist's research has been disseminated through multiple venues. Most of their publications appear in Zenodo (CERN European Organization for Nuclear Research), arXiv (Cornell University), OPAL (Open@LaTrobe) (La Trobe University), PubMed, and bioRxiv (Cold Spring Harbor Laboratory).

Best Publications

  • Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

    Kevin Knight;Ani Nenkova;Owen Rambow

  • CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset

    Houwei Cao;David G. Cooper;Michael K. Keutmann;Ruben C. Gur

  • A SURVEY OF TEXT SUMMARIZATION TECHNIQUES

    Ani Nenkova;Kathleen R. McKeown

  • Automatic Summarization

    Ani Nenkova;Sameer Maskey;Yang Liu

  • Evaluating Content Selection in Summarization: The Pyramid Method

    Ani Nenkova;Rebecca J. Passonneau

  • Revisiting Readability: A Unified Framework for Predicting Text Quality

    Emily Pitler;Ani Nenkova

  • Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion

    Lucy Vanderwende;Hisami Suzuki;Chris Brockett;Ani Nenkova

  • Tracking and summarizing news on a daily basis with Columbia's Newsblaster

    Kathleen R. McKeown;Regina Barzilay;David Evans;Vasileios Hatzivassiloglou

  • The Pyramid Method: Incorporating human content selection variation in summarization evaluation

    Ani Nenkova;Rebecca Passonneau;Kathleen McKeown

  • Automatic sense prediction for implicit discourse relations in text

    Emily Pitler;Annie Louis;Ani Nenkova

  • The Impact of Frequency on Summarization

    A. Nenkova;L. Vanderwende;Lucy Vanderwende

  • Using Syntax to Disambiguate Explicit Discourse Connectives in Text

    Emily Pitler;Ani Nenkova

  • Class-level spectral features for emotion recognition

    Dmitri Bitouk;Ragini Verma;Ani Nenkova

  • A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization

    Ani Nenkova;Lucy Vanderwende;Kathleen McKeown

  • A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature.

    Benjamin E. Nye;Junyi Jessy Li;Roma Patel;Yinfei Yang

  • High Frequency Word Entrainment in Spoken Dialogue

    Ani Nenkova;Agust'in Gravano;Julia Hirschberg

  • Automatically assessing machine summary content without a gold standard

    Annie Louis;Ani Nenkova

  • Easily Identifiable Discourse Relations

    Emily Pitler;Mridhula Raghupathy;Hena Mehta;Ani Nenkova

  • Automatic text summarization of newswire: lessons learned from the document understanding conference

    Ani Nenkova

  • Improving the Estimation of Word Importance for News Multi-Document Summarization

    Kai Hong;Ani Nenkova

Frequent Co-Authors

Kathleen R. McKeown
Kathleen R. McKeown Columbia University
Byron C. Wallace
Byron C. Wallace Northeastern University
Dan Jurafsky
Dan Jurafsky Stanford University
Rebecca J. Passonneau
Rebecca J. Passonneau Pennsylvania State University
Julia Hirschberg
Julia Hirschberg Columbia University
Vasileios Hatzivassiloglou
Vasileios Hatzivassiloglou Columbia University
Judith L. Klavans
Judith L. Klavans University of Maryland, College Park
Lucy Vanderwende
Lucy Vanderwende University of Washington
Ruben C. Gur
Ruben C. Gur University of Pennsylvania

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