World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
16724
World Ranking
5758
National Ranking
2618

Philip K. Chan 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 Philip K. Chan 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: 113 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Philip K. Chan 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 Philip K. Chan 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Philip K. Chan is affiliated with the Florida Institute of Technology in the United States. Their research spans multiple disciplines, prominently focusing on Medicine and Computer Science, with significant contributions in subfields such as Artificial Intelligence, Psychiatry and Mental Health, Pulmonary and Respiratory Medicine, Oncology, and Astronomy and Astrophysics.

Their main research topics include:

  • Domain Adaptation and Few-Shot Learning
  • Anomaly Detection Techniques and Applications
  • Bipolar Disorder and Treatment
  • Solar and Space Plasma Dynamics
  • Cancer Immunotherapy and Biomarkers
  • Advanced Malware Detection Techniques
  • Adversarial Robustness in Machine Learning

Philip K. Chan has published extensively, with recent papers covering a range of topics in both medical and computational sciences. Notable papers include:

  • A Randomized, Placebo-Controlled Pilot Study of Quetiapine-XR Monotherapy or Adjunctive Therapy to Antidepressant in Acute Major Depressive Disorder with Current Generalized Anxiety Disorder, 2025, Psychopharmacology Bulletin
  • A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections, 2022, Space Weather
  • Real-world outcomes in patients with metastatic renal cell carcinoma treated with first-line nivolumab plus ipilimumab, 2021, Journal of Clinical Oncology
  • Representation Learning with Function Call Graph Transformations for Malware Open Set Recognition, 2022, 2022 International Joint Conference on Neural Networks (IJCNN)
  • Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition, 2022, arXiv (Cornell University)

Their frequent publication venues reflect a diverse engagement with both medical and technological research areas:

  • arXiv (Cornell University)
  • British Journal of Urology
  • Psychopharmacology Bulletin
  • Space Weather
  • Journal of Clinical Oncology

Philip K. Chan collaborates regularly with several researchers, including Jingyun Jia, B.D. Kelly, David E. Kemp, and Carla Conroy. These collaborations are evident in multiple joint publications across various topics.

Best Publications

  • Toward accurate dynamic time warping in linear time and space

    Stan Salvador;Philip Chan

  • Distributed data mining in credit card fraud detection

    P.K. Chan;W. Fan;A.L. Prodromidis;S.J. Stolfo

  • AdaCost: Misclassification Cost-Sensitive Boosting

    Wei Fan;Salvatore J. Stolfo;Junxin Zhang;Philip K. Chan

  • Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms

    S. Salvador;P. Chan

  • Cost-based modeling for fraud and intrusion detection: results from the JAM project

    S.J. Stolfo;Wei Fan;Wenke Lee;A. Prodromidis

  • An analysis of the 1999 DARPA/lincoln Laboratory evaluation data for network anomaly detection

    Matthew V. Mahoney;Philip K. Chan

  • Learning nonstationary models of normal network traffic for detecting novel attacks

    Matthew V. Mahoney;Philip K. Chan

  • Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection

    Philip K. Chan;Salvatore J. Stolfo

  • Learning Patterns from Unix Process Execution Traces for Intrusion Detection

    Wenke Lee;Saivatore J. Stolfo;Philip K. Chan

  • JAM: java agents for meta-learning over distributed databases

    Salvatore Stolfo;Andreas L. Prodromidis;Shelley Tselepis;Wenke Lee

  • Systems for knowledge discovery in databases

    C.J. Matheus;P.K. Chan;G. Piatetsky-Shapiro

  • Real time data mining-based intrusion detection

    Wenke Lee;S.J. Stolfo;P.K. Chan;E. Eskin

  • FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space

    Stan Salvador;Philip K. Chan

  • Meta-learning in distributed data mining systems: Issues and approaches

    Andreas L. Prodromidis;Philip K. Chan

  • Using artificial anomalies to detect unknown and known network intrusions

    Wei Fan;M. Miller;S.J. Stolfo;Wenke Lee

  • PHAD: packet header anomaly detection for identifying hostile network traffic

    Matthew V. Mahoney;Philip K. Chan

  • Experiments on multistrategy learning by meta-learning

    Philip K. Chan;Salvatore J. Stolfo

  • Learning rules for anomaly detection of hostile network traffic

    M.V. Mahoney;P.K. Chan

  • A comparative evaluation of voting and meta-learning on partitioned data

    Philip K. Chan;Salvatore J. Stolfo

  • Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results 1

    Salvatore J. Stolfo;David W. Fan;Wenke Lee;Andreas L. Prodromidis

  • Advances in Distributed and Parallel Knowledge Discovery

    Hillol Kargupta;Philip Chan

Frequent Co-Authors

Salvatore J. Stolfo
Salvatore J. Stolfo Columbia University
Wenke Lee
Wenke Lee Georgia Institute of Technology
Hillol Kargupta
Hillol Kargupta University of Maryland, Baltimore County
Wei Fan
Wei Fan Tencent (China)
Debasis Mitra
Debasis Mitra Columbia University
Eleazar Eskin
Eleazar Eskin University of California, Los Angeles
Carla E. Brodley
Carla E. Brodley Northeastern University
Ouri Wolfson
Ouri Wolfson University of Illinois at Chicago
Zoran Obradovic
Zoran Obradovic Temple University
Vipin Kumar
Vipin Kumar University of Minnesota

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