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D-Index & Metrics

Computer Science

D-Index
60
Citations
11985
World Ranking
3289
National Ranking
1592

Gail E. Kaiser 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 Gail E. Kaiser 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: 375 publications — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Gail E. Kaiser 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 Gail E. Kaiser 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Gail E. Kaiser is affiliated with Columbia University in the United States. Their research contributions span multiple areas within computer science, focusing notably on software engineering and related subfields.

The main field of study for Kaiser is Computer Science, with a strong emphasis on several subfields including Software, Information Systems, Molecular Biology, Signal Processing, and Computer Networks and Communications.

The research topics commonly addressed by Kaiser include:

  • Software Engineering Research
  • Software Testing and Debugging Techniques
  • Advanced Malware Detection Techniques
  • Software Reliability and Analysis Research
  • Software System Performance and Reliability
  • Autonomous Vehicle Technology and Safety
  • Genomics and Phylogenetic Studies

Recent publications authored or co-authored by Kaiser are as follows:

  • "Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles," 2022, IEEE Transactions on Software Engineering
  • "VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements," 2022, 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
  • "Sequence Model Design for Code Completion in the Modern IDE," 2020, arXiv (Cornell University)
  • "CYCLE: Learning to Self-Refine the Code Generation," 2024, Proceedings of the ACM on Programming Languages
  • "Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles," 2021, arXiv (Cornell University)

Kaiser frequently publishes in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Software Engineering
  • 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
  • Proceedings of the ACM on Programming Languages
  • Journal of Systems and Software

Collaborations are an important aspect of Kaiser's work, with common co-authors including Baishakhi Ray, Yangruibo Ding, Marcus J. Min, Alessandro Morari, and Saurabh Pujar.

Best Publications

  • An information retrieval approach for automatically constructing software libraries

    Y.S. Maarek;D.M. Berry;G.E. Kaiser

  • DOM-based content extraction of HTML documents

    Suhit Gupta;Gail Kaiser;David Neistadt;Peter Grimm

  • Concurrency control in advanced database applications

    Naser S. Barghouti;Gail E. Kaiser

  • Testing and validating machine learning classifiers by metamorphic testing

    Xiaoyuan Xie;Joshua W. K. Ho;Christian Murphy;Gail Kaiser

  • Intelligent assistance for software development and maintenance

    G.E. Kaiser;P.H. Feiler;S.S. Popovich

  • Split-Transactions for Open-Ended Activities

    Calton Pu;Gail E. Kaiser;Norman C. Hutchinson

  • Adequate testing and object-oriented programming

    D. E. Perry;G. E. Kaiser

  • The Apache HTTP Server Project

    R.T. Fielding;G. Kaiser

  • Properties of Machine Learning Applications for Use in Metamorphic Testing

    Christian Murphy;Gail E. Kaiser;Lifeng Hu

  • Automating Content Extraction of HTML Documents

    Suhit Gupta;Gail E. Kaiser;Peter Grimm;Michael F. Chiang

  • A paradigm for decentralized process modeling and its realization in the Oz environment

    Israel Z. Ben-Shaul;Gail E. Kaiser

  • Kinesthetics eXtreme: an external infrastructure for monitoring distributed legacy systems

    G. Kaiser;J. Parekh;P. Gross;G. Valetto

  • Models of software development environments

    D. E. Perry;G. E. Kaiser

  • Self-managing systems: a control theory foundation

    Y. Diao;J.L. Hellerstein;Sujay Parekh;R. Griffith

  • An architecture for intelligent assistance in software development

    G. E. Kaiser;P. H. Feiler

  • Models of software development environments

    D.E. Perry;G.E. Kaiser

  • A control theory foundation for self-managing computing systems

    Yixin Diao;J.L. Hellerstein;S. Parekh;R. Griffith

  • A paradigm for decentralized process modeling and its realization in the OZ environment

    Unknown

  • Experience with Process Modeling in the Marvel Software Development Environment Kernel

    Gail E. Kaiser

  • Using tool abstraction to compose systems

    D. Garlan;G.E. Kaiser;D. Notkin

  • Melding Software Systems from Reusable Building Blocks

    G.E. Kaiser;D. Garlan

Frequent Co-Authors

Dewayne E. Perry
Dewayne E. Perry The University of Texas at Austin
Baishakhi Ray
Baishakhi Ray Columbia University
David Garlan
David Garlan Carnegie Mellon University
Salvatore J. Stolfo
Salvatore J. Stolfo Columbia University
Roger N. Anderson
Roger N. Anderson Columbia University
Cynthia Rudin
Cynthia Rudin Duke University
Joseph L. Hellerstein
Joseph L. Hellerstein University of Washington
Yixin Diao
Yixin Diao IBM (United States)
Yoelle Maarek
Yoelle Maarek Amazon (United States)
Justin Starren
Justin Starren Northwestern University

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