World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
17983
World Ranking
3981
National Ranking
114

James Bailey 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 James Bailey 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: 349 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

James Bailey 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 James Bailey 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

James Bailey is affiliated with the University of Melbourne in Australia. Their research primarily centers on computer science, with a concentration in artificial intelligence, computer vision and pattern recognition, signal processing, human-computer interaction, and molecular biology.

The main topics of their work include:

  • Adversarial Robustness in Machine Learning
  • Anomaly Detection Techniques and Applications
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Integrated Circuits and Semiconductor Failure Analysis

James Bailey has contributed extensively to various academic venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • APL Machine Learning
  • CHI Conference on Human Factors in Computing Systems
  • Proceedings of the Annual Hawaii International Conference on System Sciences

Coauthors frequently collaborating with Bailey consist of:

  • Xingjun Ma
  • Sarah Erfani
  • Hanxun Huang
  • Yisen Wang
  • Qiuhong Ke

Recent publications highlight Bailey's work on adversarial robustness and machine learning techniques. Selected papers include:

  • Understanding adversarial attacks on deep learning based medical image analysis systems, 2020, Pattern Recognition
  • On the Convergence and Robustness of Adversarial Training, 2021, arXiv (Cornell University)
  • Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets, 2020, arXiv (Cornell University)
  • Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression, 2021, arXiv (Cornell University)
  • Normalized Loss Functions for Deep Learning with Noisy Labels, 2020, arXiv (Cornell University)

Best Publications

  • Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

    Nguyen Xuan Vinh;Julien Epps;James Bailey

  • Information theoretic measures for clusterings comparison: is a correction for chance necessary?

    Nguyen Xuan Vinh;Julien Epps;James Bailey

  • Symmetric Cross Entropy for Robust Learning With Noisy Labels

    Yisen Wang;Xingjun Ma;Zaiyi Chen;Yuan Luo

  • Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

    Xingjun Ma;Bo Li;Yisen Wang;Sarah M. Erfani

  • Understanding adversarial attacks on deep learning based medical image analysis systems

    Xingjun Ma;Yuhao Niu;Lin Gu;Yisen Wang

  • Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks

    Yunfei Liu;Xingjun Ma;James Bailey;Feng Lu

  • Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

    James Bailey;Alistair Moffat;Charu C. Aggarwal;Maarten de Rijke

  • Iterative Learning with Open-set Noisy Labels

    Yisen Wang;Weiyang Liu;Xingjun Ma;James Bailey

  • Improving Adversarial Robustness Requires Revisiting Misclassified Examples

    Yisen Wang;Difan Zou;Jinfeng Yi;James Bailey

  • Dimensionality-Driven Learning with Noisy Labels

    Xingjun Ma;Yisen Wang;Michael E. Houle;Shuo Zhou

  • Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization

    James Bailey;Peter J. Stuckey

  • Identifying at-risk students in massive open online courses

    Jiazhen He;James Bailey;Benjamin I. P. Rubinstein;Rui Zhang

  • Normalized Loss Functions for Deep Learning with Noisy Labels

    Xingjun Ma;Hanxun Huang;Yisen Wang;Simone Romano

  • Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis

    R. Garnavi;M. Aldeen;J. Bailey

  • Clean-Label Backdoor Attacks on Video Recognition Models

    Shihao Zhao;Xingjun Ma;Xiang Zheng;James Bailey

  • Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles

    Ranjie Duan;Xingjun Ma;Yisen Wang;James Bailey

  • Mining minimal distinguishing subsequence patterns with gap constraints

    Xiaonan Ji;J. Bailey;Guozhu Dong

  • Adjusting for chance clustering comparison measures

    Simone Romano;Nguyen Xuan Vinh;James Bailey;Karin Verspoor

  • is-rSNP

    Geoff Macintyre;James Bailey;Izhak Haviv;Izhak Haviv;Izhak Haviv;Adam Kowalczyk

  • An Event-Condition-Action Language for XML.

    James Bailey;George Papamarkos;Alexandra Poulovassilis;Peter T. Wood

  • An event-condition-action language for XML

    James Bailey;Alexandra Poulovassilis;Peter T. Wood

  • On the convergence and robustness of adversarial training

    Yisen Wang;Xingjun Ma;James Bailey;Jinfeng Yi

  • Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

    Dongxian Wu;Yisen Wang;Shu-Tao Xia;James Bailey

Frequent Co-Authors

Kotagiri Ramamohanarao
Kotagiri Ramamohanarao University of Melbourne
Christopher Leckie
Christopher Leckie University of Melbourne
Wassim M. Haddad
Wassim M. Haddad Georgia Institute of Technology
Gregor Kennedy
Gregor Kennedy University of Melbourne
Lars Kulik
Lars Kulik University of Melbourne
Guozhu Dong
Guozhu Dong Wright State University
Peter J. Stuckey
Peter J. Stuckey Monash University
Jian Pei
Jian Pei Duke University
Wei Liu
Wei Liu University of Technology Sydney
François Bry
François Bry Ludwig-Maximilians-Universität München

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