World's Best Scientists 2026 revealed!
Rachel K. E. Bellamy

Rachel K. E. Bellamy

D-Index & Metrics

Computer Science

D-Index
32
Citations
5186
World Ranking
13055
National Ranking
5256

Rachel K. E. Bellamy 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 Rachel K. E. Bellamy 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: 105 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Rachel K. E. Bellamy 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 Rachel K. E. Bellamy 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2016 - ACM Senior Member

Overview

Rachel K. E. Bellamy is affiliated with IBM in the United States and has contributed research primarily in the field of computer science with a focus on artificial intelligence. Their work encompasses various subfields including artificial intelligence, safety research, general decision sciences, health informatics, and sociology and political science.

Their main research topics cover explainable artificial intelligence (XAI), machine learning and data classification, machine learning and algorithms, ethics and social impacts of AI, decision-making and behavioral economics, artificial intelligence in healthcare and education, and adversarial robustness in machine learning.

Recent papers authored or coauthored by Rachel K. E. Bellamy include:

  • Explainable Active Learning (XAL), 2021, Proceedings of the ACM on Human-Computer Interaction
  • Joint Optimization of AI Fairness and Utility: A Human-Centered Approach, 2020, arXiv (Cornell University)
  • Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience, 2020, arXiv (Cornell University)
  • AI Explainability 360: Impact and Design, 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • AI Explainability 360: Impact and Design, 2021, arXiv (Cornell University)

Frequent coauthors include Yunfeng Zhang, Kush R. Varshney, Q. Vera Liao, Bhavya Ghai, and Klaus Mueller. Their collaborations have contributed to advancing research in several areas of AI and its explainability.

The primary venues for their publications are arXiv (Cornell University), Proceedings of the ACM on Human-Computer Interaction, Proceedings of the AAAI Conference on Artificial Intelligence, and the Proceedings of the AAAI/ACM Conference on AI Ethics and Society.

Rachel K. E. Bellamy holds the ACM Senior Member award since 2016, reflecting recognition within the computing community.

Best Publications

  • AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

    R. K. E. Bellamy;K. Dey;M. Hind;S. C. Hoffman

  • Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

    Yunfeng Zhang;Q. Vera Liao;Rachel K. E. Bellamy

  • AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

    Rachel K. E. Bellamy;Kuntal Dey;Michael Hind;Samuel C. Hoffman

  • FactSheets: Increasing trust in AI services through supplier's declarations of conformity

    M. Arnold;R. K. E. Bellamy;M. Hind;S. Houde

  • Designing educational technology: computer-mediated change

    R. K. E. Bellamy

  • Explaining models: an empirical study of how explanations impact fairness judgment

    Jonathan Dodge;Q. Vera Liao;Yunfeng Zhang;Rachel K. E. Bellamy

  • One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

    Vijay Arya;Rachel K. E. Bellamy;Pin-Yu Chen;Amit Dhurandhar

  • System and method for dynamically presenting a summary of content associated with a document

    Branimir Boguraev;Rachel Katherine Emma Bellamy;Yin Yin Wong

  • How Programmers Debug, Revisited: An Information Foraging Theory Perspective

    J. Lawrance;C. Bogart;M. Burnett;R. Bellamy

  • Moving into a new software project landscape

    Barthelemy Dagenais;Harold Ossher;Rachel K. E. Bellamy;Martin P. Robillard

  • Putting it all together: towards a pattern language for interaction design: A CHI 97 workshop

    Elisabeth Bayle;Rachel Bellamy;George Casaday;Thomas Erickson

  • Face Value? Exploring the Effects of Embodiment for a Group Facilitation Agent

    Ameneh Shamekhi;Q. Vera Liao;Dakuo Wang;Rachel K. E. Bellamy

  • BlogCentral: the role of internal blogs at work

    Jina Huh;Lauretta Jones;Thomas Erickson;Wendy A. Kellogg

  • Smalltalk scaffolding: a case study of minimalist instruction

    Mary Beth Rosson;John M. Carrol;Rachel K. E. Bellamy

  • Trials and tribulations of developers of intelligent systems: A field study

    Charles Hilllaz;Rachel Bellarnyz;Thomas Ericksonz;Margaret Burnett

  • An Information Foraging Theory Perspective on Tools for Debugging, Refactoring, and Reuse Tasks

    Scott D. Fleming;Chris Scaffidi;David Piorkowski;Margaret Burnett

  • Using information scent to model the dynamic foraging behavior of programmers in maintenance tasks

    Joseph Lawrance;Rachel Bellamy;Margaret Burnett;Kyle Rector

  • Parsing and Gnisrap: a model of device use

    T. R. G. Green;R. K. E. Bellamy;M. Parker

  • Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers

    Bhavya Ghai;Q. Vera Liao;Yunfeng Zhang;Rachel Bellamy

  • Advances in human-computer interaction.

    Unknown

  • The whats and hows of programmers' foraging diets

    David J. Piorkowski;Scott D. Fleming;Irwin Kwan;Margaret M. Burnett

  • Deploying CogTool: integrating quantitative usability assessment into real-world software development

    Rachel Bellamy;Bonnie John;Sandra Kogan

  • Reactive information foraging: an empirical investigation of theory-based recommender systems for programmers

    David Piorkowski;Scott Fleming;Christopher Scaffidi;Christopher Bogart

Frequent Co-Authors

Bonnie E. John
Bonnie E. John IBM (United States)
Thomas Erickson
Thomas Erickson Independent Scientist / Consultant, US
Kush R. Varshney
Kush R. Varshney IBM (United States)
Aleksandra Mojsilovic
Aleksandra Mojsilovic IBM (United States)
Michael Hind
Michael Hind IBM (United States)
Wendy A. Kellogg
Wendy A. Kellogg IBM (United States)
Margaret Burnett
Margaret Burnett Oregon State University
Brian P. Gaucher
Brian P. Gaucher IBM (United States)
Jeffrey O. Kephart
Jeffrey O. Kephart IBM (United States)
Pin-Yu Chen
Pin-Yu Chen IBM (United States)

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