World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
11650
World Ranking
6100
National Ranking
190

Liming Zhu 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 Liming Zhu 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: 283 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

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

Liming Zhu 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 Liming Zhu 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Liming Zhu is affiliated with the Commonwealth Scientific and Industrial Research Organisation in Australia. Their research primarily focuses on various aspects of computer science, with a significant emphasis on artificial intelligence, information systems, and safety research.

The scientist has contributed to the fields of artificial intelligence and its ethics, blockchain technology applications and security, privacy-preserving technologies in data, and software engineering research. Their work also spans artificial intelligence in healthcare and education, explainable artificial intelligence (XAI), and adversarial robustness in machine learning.

Liming Zhu's frequent co-authors include Xiwei Xu, Qinghua Lu (with two separate collaboration records), Zhenchang Xing, and Jon Whittle. These collaborations reflect ongoing partnerships in their research endeavors.

Key venues where Liming Zhu has published include:

  • arXiv (Cornell University)
  • IEEE Software
  • SSRN Electronic Journal
  • Journal of Systems and Software
  • ACM Transactions on Software Engineering and Methodology

Among recent papers, Liming Zhu has contributed to research on blockchain and federated learning, generative adversarial networks in collaborative human-AI applications, and trustworthy AI architectures. Selected papers include:

  • "Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT," 2020, IEEE Internet of Things Journal
  • "Generative Adversarial Networks-Enabled Human-Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends," 2021, Frontiers in Artificial Intelligence
  • "Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems," 2022, IEEE Internet of Things Journal
  • "Wireframe-based UI Design Search through Image Autoencoder," 2020, ACM Transactions on Software Engineering and Methodology
  • "A systematic literature review on blockchain governance," 2022, Journal of Systems and Software

Best Publications

  • A Taxonomy of Blockchain-Based Systems for Architecture Design

    Xiwei Xu;Ingo Weber;Mark Staples;Liming Zhu

  • Devops: A Software Architect's Perspective

    Len Bass;Ingo Weber;Liming Zhu

  • Blockchains for Business Process Management - Challenges and Opportunities

    Jan Mendling;Ingo Weber;Wil Van Der Aalst;Jan Vom Brocke

  • Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices

    Mojtaba Shahin;Muhammad Ali Babar;Liming Zhu

  • The Blockchain as a Software Connector

    Xiwei Xu;Cesare Pautasso;Liming Zhu;Vincent Gramoli

  • Blockchain Based Data Integrity Service Framework for IoT Data

    Bin Liu;Xiao Liang Yu;Shiping Chen;Xiwei Xu

  • Adversarial Examples for Graph Data: Deep Insights into Attack and Defense.

    Huijun Wu;Huijun Wu;Chen Wang;Yuriy Tyshetskiy;Andrew Docherty

  • Software quality and agile methods

    Ming Huo;J. Verner;Liming Zhu;M.A. Babar

  • A framework for classifying and comparing software architecture evaluation methods

    M.A. Babar;L. Zhu;R. Jeffery

  • Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT

    Weishan Zhang;Qinghua Lu;Qiuyu Yu;Zhaotong Li

  • Designing blockchain-based applications a case study for imported product traceability

    Xiwei Xu;Xiwei Xu;Qinghua Lu;Qinghua Lu;Yue Liu;Liming Zhu;Liming Zhu

  • DevOps and Its Practices

    Liming Zhu;Len Bass;George Champlin-Scharff

  • MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

    Manqing Dong;Feng Yuan;Lina Yao;Xiwei Xu

  • Tradeoff and Sensitivity Analysis in Software Architecture Evaluation Using Analytic Hierarchy Process

    Liming Zhu;Aybüke Aurum;Ian Gorton;Ross Jeffery

  • Analysis of Blockchain Solutions for IoT: A Systematic Literature Review

    Sin Kuang Lo;Yue Liu;Su Yen Chia;Xiwei Xu

  • A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective

    Sin Kit Lo;Qinghua Lu;Chen Wang;Hye-Young Paik

  • Evaluating guidelines for reporting empirical software engineering studies

    Barbara Kitchenham;Hiyam Al-Khilidar;Muhammed Ali Babar;Mike Berry

  • Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

    Huijun Wu;Chen Wang;Yuriy Tyshetskiy;Andrew Docherty

  • IoTChain: Establishing Trust in the Internet of Things Ecosystem Using Blockchain

    Bin Yu;Jarod Wright;Surya Nepal;Liming Zhu

  • Generative Adversarial Networks-Enabled Human-Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends.

    Rowan T. Hughes;Liming Zhu;Tomasz Bednarz;Tomasz Bednarz

  • A Pattern Collection for Blockchain-based Applications

    Xiwei Xu;Cesare Pautasso;Liming Zhu;Qinghua Lu

  • Object detection for graphical user interface: old fashioned or deep learning or a combination?

    Jieshan Chen;Mulong Xie;Zhenchang Xing;Chunyang Chen

  • Blockchains for Business Process Management - Challenges and Opportunities

    Jan Mendling;Ingo Weber;Wil van der Aalst;Jan vom Brocke

Frequent Co-Authors

Xiwei Xu
Xiwei Xu Commonwealth Scientific and Industrial Research Organisation
Len Bass
Len Bass Carnegie Mellon University
Ingo Weber
Ingo Weber Technical University of Berlin
Sherif Sakr
Sherif Sakr University of Tartu
Muhammad Ali Babar
Muhammad Ali Babar University of Adelaide
Ian Gorton
Ian Gorton Northeastern University
Lina Yao
Lina Yao Commonwealth Scientific and Industrial Research Organisation
Zhenchang Xing
Zhenchang Xing Australian National University
Chunyang Chen
Chunyang Chen Technical University of Munich
Alan Fekete
Alan Fekete University of Sydney

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