World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
15428
World Ranking
6319
National Ranking
2824

Pang-Ning Tan 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 Pang-Ning Tan 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: 183 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Pang-Ning Tan 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 Pang-Ning Tan 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Pang-Ning Tan is affiliated with Michigan State University in the United States. Their research covers significant areas within computer science and environmental science, contributing to both fields through numerous publications spanning several subfields and topics.

Their main fields of study include:

  • Computer Science
  • Environmental Science

Within these fields, their work further specializes into the following subfields:

  • Artificial Intelligence
  • Global and Planetary Change
  • Epidemiology
  • Public Health, Environmental and Occupational Health
  • Electrical and Electronic Engineering

The primary research topics covered in their work are:

  • Advanced Graph Neural Networks
  • Vaccine Coverage and Hesitancy
  • Climate Variability and Models
  • Energy Load and Power Forecasting
  • Hydrology and Drought Analysis
  • Complex Network Analysis Techniques
  • Soil Moisture and Remote Sensing

Pang-Ning Tan has contributed frequently to various publication venues including:

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • arXiv (Cornell University)
  • Proceedings of the Annual Hawaii International Conference on System Sciences
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • ISPRS Journal of Photogrammetry and Remote Sensing

Their recent scholarly papers include:

  • "Bursting the Filter Bubble: Fairness-Aware Network Link Prediction," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study," 2021, JMIR Public Health and Surveillance
  • "Unsupervised Anomaly Detection by Robust Density Estimation," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Review of synthetic aperture radar with deep learning in agricultural applications," 2024, ISPRS Journal of Photogrammetry and Remote Sensing
  • "Yield estimation from SAR data using patch-based deep learning and machine learning techniques," 2024, Computers and Electronics in Agriculture

Frequent collaborators in their research include:

  • Lifeng Luo
  • Francisco C. Santos
  • Abdol-Hossein Esfahanian
  • Farzan Masrour
  • Tyler Wilson

Best Publications

  • Web usage mining: discovery and applications of usage patterns from Web data

    Jaideep Srivastava;Robert Cooley;Mukund Deshpande;Pang-Ning Tan

  • Introduction to Data Mining, (First Edition)

    Pang-Ning Tan;Michael Steinbach;Vipin Kumar

  • Selecting the right interestingness measure for association patterns

    Pang-Ning Tan;Vipin Kumar;Jaideep Srivastava

  • Selecting the right objective measure for association analysis

    Pang-Ning Tan;Vipin Kumar;Jaideep Srivastava

  • Discovery of Web Robot Sessions Based on their Navigational Patterns

    Pang-Ning Tan;Vipin Kumar

  • Data Mining for Network Intrusion Detection

    Paul Dokas;Levent Ertoz;Vipin Kumar;Aleksandar Lazarevic

  • Multistep-ahead time series prediction

    Haibin Cheng;Pang-Ning Tan;Jing Gao;Jerry Scripps

  • Interestingness Measures for Association Patterns: A Perspective

    Pang-ning Tan;Vipin Kumar

  • Discovery of Interesting Usage Patterns from Web Data

    Robert Cooley;Pang-Ning Tan;Jaideep Srivastava

  • Cross‐scale interactions: quantifying multi‐scaled cause–effect relationships in macrosystems

    Patricia A. Soranno;Kendra S. Cheruvelil;Edward G. Bissell;Mary T. Bremigan

  • Hyperclique pattern discovery

    Hui Xiong;Pang-Ning Tan;Vipin Kumar

  • Mining strong affinity association patterns in data sets with skewed support distribution

    H. Xiong;P.-N. Tan;Vipin Kumar

  • Receiver Operating Characteristic.

    Pang-Ning Tan

  • Discovery of climate indices using clustering

    Michael Steinbach;Pang-Ning Tan;Vipin Kumar;Steven Klooster

  • Converting Output Scores from Outlier Detection Algorithms into Probability Estimates

    Jing Gao;Pang-Ning Tan

  • Detection and characterization of anomalies in multivariate time series

    Haibin Cheng;Pang-Ning Tan;Christopher Potter;Steven A. Klooster

  • Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs

    Hui Xiong;Shashi Shekhar;Pang-Ning Tan;Vipin Kumar

  • Introduction to Data Mining (2nd Edition)

    Pang-Ning Tan;Michael Steinbach;Anuj Karpatne;Vipin Kumar

  • LAGOS-NE: A multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

    Patricia A. Soranno;Linda C. Bacon;Michael Beauchene;Karen E. Bednar

  • On ontology-driven document clustering using core semantic features

    Samah Fodeh;Bill Punch;Pang-Ning Tan

  • Proceedings of the 2014 SIAM International Conference on Data Mining

    Mohammed Zaki;Zoran Obradovic;Pang Ning Tan;Arindam Banerjee

Frequent Co-Authors

Vipin Kumar
Vipin Kumar University of Minnesota
Jiayu Zhou
Jiayu Zhou Michigan State University
Patricia A. Soranno
Patricia A. Soranno Michigan State University
Lifeng Luo
Lifeng Luo Michigan State University
Tyler Wagner
Tyler Wagner United States Geological Survey
Jaideep Srivastava
Jaideep Srivastava University of Minnesota
Christopher Potter
Christopher Potter University of Cambridge
Jing Gao
Jing Gao Purdue University West Lafayette
Hui Xiong
Hui Xiong Rutgers, The State University of New Jersey
Anil K. Jain
Anil K. Jain Michigan State University

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