World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
13852
World Ranking
12348
National Ranking
783

Victor Lavrenko 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 Victor Lavrenko 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: 82 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

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

Victor Lavrenko 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 Victor Lavrenko 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Victor Lavrenko is affiliated with the University of Edinburgh in the United Kingdom. Their research primarily focuses on medicine, with a particular emphasis on infectious diseases, epidemiology, and statistical and nonlinear physics. Their work also touches on related fields such as sociology and political science, and neurology.

Their recent scholarly contributions include a variety of papers published in notable venues. These papers are:

  • RT to Win! Predicting Message Propagation in Twitter, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • Morbidity of SARS-CoV-2 in the evolution to endemicity and in comparison with influenza, 2024, Communications Medicine
  • Comparative Organ Disease Burden and Sequelae of Influenza and SARS-CoV-2 Infection: An Observational Study Using Real-World Data, 2023, bioRxiv (Cold Spring Harbor Laboratory)
  • Author Correction: Morbidity of SARS-CoV-2 in the evolution to endemicity and in comparison with influenza, 2025, Communications Medicine

The main topics addressed in their research include misinformation and its impacts, complex network analysis techniques, opinion dynamics and social influence, as well as SARS-CoV-2 detection and testing. Their studies also cover SARS-CoV-2 and COVID-19 research and respiratory viral infections, along with influenza virus research studies.

Victor Lavrenko has collaborated frequently with a number of co-authors, including István Bartha, M. Cyrus Maher, Keith Boundy, Elizabeth Kinter, and Wendy W. Yeh. Each of these collaborators has partnered on multiple occasions, contributing to several key publications.

Their publications appear often in the journal Communications Medicine, as well as the Proceedings of the International AAAI Conference on Web and Social Media and bioRxiv (Cold Spring Harbor Laboratory).

Best Publications

  • Relevance-Based Language Models

    Victor Lavrenko;W. Bruce Croft

  • Automatic image annotation and retrieval using cross-media relevance models

    J. Jeon;V. Lavrenko;R. Manmatha

  • Multiple Bernoulli relevance models for image and video annotation

    S.L. Feng;R. Manmatha;V. Lavrenko

  • On-line new event detection and tracking

    James Allan;Ron Papka;Victor Lavrenko

  • A Model for Learning the Semantics of Pictures

    Victor Lavrenko;R. Manmatha;Jiwoon Jeon

  • Streaming First Story Detection with application to Twitter

    Saša Petrović;Miles Osborne;Victor Lavrenko

  • On-line new event detection and tracking

    Unknown

  • RT to Win! Predicting Message Propagation in Twitter

    Sasa Petrovic;Miles Osborne;Victor Lavrenko

  • Holistic word recognition for handwritten historical documents

    V. Lavrenko;T.M. Rath;R. Manmatha

  • Mining of Concurrent Text and Time Series

    Victor Lavrenko;Matt Schmill;Dawn Lawrie;Paul Ogilvie

  • Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002

    James Allan;Jay Aslam;Nicholas Belkin;Chris Buckley

  • Cross-lingual relevance models

    Victor Lavrenko;Martin Choquette;W. Bruce Croft

  • First story detection in TDT is hard

    James Allan;Victor Lavrenko;Hubert Jin

  • Language models for financial news recommendation

    Victor Lavrenko;Matt Schmill;Dawn Lawrie;Paul Ogilvie

  • The Edinburgh Twitter Corpus

    Saša Petrović;Miles Osborne;Victor Lavrenko

  • Profiling of Short-Tandem-Repeat Disease Alleles in 12,632 Human Whole Genomes

    Haibao Tang;Ewen F. Kirkness;Christoph Lippert;William H. Biggs

  • A Generative Theory of Relevance

    Victor Lavrenko

  • Relevance Feedback and Personalization: A Language Modeling Perspective.

    W. Bruce Croft;Stephen Cronen-Townsend;Victor Lavrenko

  • A search engine for historical manuscript images

    Toni M. Rath;R. Manmatha;Victor Lavrenko

  • Identification of individuals by trait prediction using whole-genome sequencing data

    Christoph Lippert;Riccardo Sabatini;M. Cyrus Maher;Eun Yong Kang

  • Relevance models for topic detection and tracking

    Victor Lavrenko;James Allan;Edward DeGuzman;Daniel LaFlamme

  • Sentiment Retrieval using Generative Models

    Koji Eguchi;Victor Lavrenko

Frequent Co-Authors

James Allan
James Allan University of Massachusetts Amherst
Miles Osborne
Miles Osborne Bloomberg LP
R. Manmatha
R. Manmatha Amazon (United States)
Amalio Telenti
Amalio Telenti VIR Biotechnology (United States)
W. Bruce Croft
W. Bruce Croft University of Massachusetts Amherst
Franz Josef Och
Franz Josef Och Google (United States)
Haibao Tang
Haibao Tang Fujian Agriculture and Forestry University
C. Thomas Caskey
C. Thomas Caskey Baylor College of Medicine
J. Craig Venter
J. Craig Venter J. Craig Venter Institute
Bing Ren
Bing Ren New York Genome Center

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