World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
104
Citations
62208
World Ranking
299
National Ranking
164

Eamonn Keogh 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 Eamonn Keogh 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 335 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Eamonn Keogh 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 Eamonn Keogh sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 104 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

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

Overview

Eamonn Keogh is affiliated with the University of California, Riverside in the United States. Their research lies predominantly within the field of Computer Science, with a focus on several related subfields including Signal Processing, Artificial Intelligence, Economics and Econometrics, Computer Vision and Pattern Recognition, and Geophysics.

The main topics covered in the scientist's work highlight a specialization in time-sensitive data analysis and pattern recognition. These topics include:

  • Time Series Analysis and Forecasting
  • Anomaly Detection Techniques and Applications
  • Complex Systems and Time Series Analysis
  • Advanced Text Analysis Techniques
  • Data Visualization and Analytics
  • Music and Audio Processing
  • Data Stream Mining Techniques

Significant publication venues where Eamonn Keogh has contributed include:

  • arXiv (Cornell University)
  • Data Mining and Knowledge Discovery
  • 2022 IEEE 38th International Conference on Data Engineering (ICDE)
  • 2022 IEEE International Conference on Data Mining (ICDM)
  • Institutional Research Information System University of Ferrara (University of Ferrara)

On frequent collaboration, the scientist has worked extensively with several researchers, including:

  • Chin-Chia Michael Yeh
  • Junpeng Wang
  • Zhongfang Zhuang
  • Ryan Mercer
  • Audrey Der

The following are some recent papers authored or co-authored by Eamonn Keogh, illustrating their research interests and areas of expertise:

  • Knowledge extraction with interval temporal logic decision trees (2020), Institutional Research Information System University of Ferrara (University of Ferrara)
  • Matrix profile goes MAD: variable-length motif and discord discovery in data series (2020), Data Mining and Knowledge Discovery
  • Time series motifs discovery under DTW allows more robust discovery of conserved structure (2021), Data Mining and Knowledge Discovery
  • FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates (2020), IEEE Transactions on Knowledge and Data Engineering
  • Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams (2022), Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Best Publications

  • Exact indexing of dynamic time warping

    Eamonn Keogh;Chotirat Ann Ratanamahatana

  • A symbolic representation of time series, with implications for streaming algorithms

    Jessica Lin;Eamonn Keogh;Stefano Lonardi;Bill Chiu

  • On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration

    Eamonn Keogh;Shruti Kasetty

  • Dimensionality reduction for fast similarity search in large time series databases

    Eamonn J. Keogh;Kaushik Chakrabarti;Michael J. Pazzani;Sharad Mehrotra

  • Experiencing SAX: a novel symbolic representation of time series

    Jessica Lin;Eamonn Keogh;Li Wei;Stefano Lonardi

  • Exact indexing of dynamic time warping

    Eamonn Keogh

  • Querying and mining of time series data: experimental comparison of representations and distance measures

    Hui Ding;Goce Trajcevski;Peter Scheuermann;Xiaoyue Wang

  • The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

    Anthony Bagnall;Jason Lines;Aaron Bostrom;James Large

  • An online algorithm for segmenting time series

    E. Keogh;S. Chu;D. Hart;M. Pazzani

  • Derivative Dynamic Time Warping.

    Eamonn J. Keogh;Michael J. Pazzani

  • Searching and mining trillions of time series subsequences under dynamic time warping

    Thanawin Rakthanmanon;Bilson Campana;Abdullah Mueen;Gustavo Batista

  • Time series shapelets: a new primitive for data mining

    Lexiang Ye;Eamonn Keogh

  • Locally adaptive dimensionality reduction for indexing large time series databases

    Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra

  • Experimental comparison of representation methods and distance measures for time series data

    Xiaoyue Wang;Abdullah Mueen;Hui Ding;Goce Trajcevski

  • Scaling up dynamic time warping for datamining applications

    Eamonn J. Keogh;Michael J. Pazzani

  • HOT SAX: efficiently finding the most unusual time series subsequence

    E. Keogh;J. Lin;A. Fu

  • Segmenting Time Series: A Survey and Novel Approach

    Eamonn Keogh;Selina Chu;David Hart;Michael Pazzani

  • The UCR time series archive

    Hoang Anh Dau;Anthony Bagnall;Kaveh Kamgar;Chin-Chia Michael Yeh

  • Clustering of time-series subsequences is meaningless: implications for previous and future research

    Eamonn Keogh;Jessica Lin

  • Towards parameter-free data mining

    Eamonn Keogh;Stefano Lonardi;Chotirat Ann Ratanamahatana

  • Chapter 36 – Exact Indexing of Dynamic Time Warping

    Eamonn Keogh

  • Experimental Comparison of Representation Methods and Distance Measures for Time Series Data

    Xiaoyue Wang;Hui Ding;Goce Trajcevski;Peter Scheuermann

Frequent Co-Authors

Abdullah Mueen
Abdullah Mueen University of New Mexico
Stefano Lonardi
Stefano Lonardi University of California, Riverside
Gustavo E. A. P. A. Batista
Gustavo E. A. P. A. Batista University of New South Wales
Michael J. Pazzani
Michael J. Pazzani University of California, Riverside
Michail Vlachos
Michail Vlachos University of Lausanne
Dimitrios Gunopulos
Dimitrios Gunopulos National and Kapodistrian University of Athens
Themis Palpanas
Themis Palpanas Université Paris Cité
Ada Wai-Chee Fu
Ada Wai-Chee Fu Chinese University of Hong Kong
Philip Brisk
Philip Brisk University of California, Riverside
Geoffrey I. Webb
Geoffrey I. Webb Monash University

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