World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
9572
World Ranking
6795
National Ranking
910

Chi Harold Liu 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 Chi Harold Liu 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: 168 publications — 34th percentile

34% of scientists in this discipline score the same or lower.

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

Chi Harold Liu 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 Chi Harold Liu 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: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Overview

Chi Harold Liu is affiliated with the Beijing Institute of Technology in China and has a research focus in computer science, particularly within artificial intelligence and its applications.

Their recent research contributions include:

  • SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Causality Inspired Representation Learning for Domain Generalization, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Deep Residual Correction Network for Partial Domain Adaptation, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Active Learning for Domain Adaptation: An Energy-Based Approach, 2022, Proceedings of the AAAI Conference on Artificial Intelligence

The scientist's collaborations are frequent with several co-authors, including:

  • Shuang Li
  • Guoren Wang
  • Rui Han
  • Guozheng Li
  • Lydia Y. Chen

Chi Harold Liu has published extensively in multiple venues, with a concentration in these areas:

  • arXiv (Cornell University)
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Journal on Selected Areas in Communications
  • IEEE Transactions on Parallel and Distributed Systems

The primary field of study is computer science, with subfields that include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Information Systems
  • Computer Science Applications

Their main research topics cover:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Neural Network Applications
  • Privacy-Preserving Technologies in Data
  • Mobile Crowdsensing and Crowdsourcing
  • COVID-19 diagnosis using AI
  • Data Visualization and Analytics

Best Publications

  • A Survey on Internet of Things From Industrial Market Perspective

    Charith Perera;Chi Harold Liu;Srimal Jayawardena;Min Chen

  • The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

    Charith Perera;Chi Harold Liu;Srimal Jayawardena

  • Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

    Chi Harold Liu;Zheyu Chen;Jian Tang;Jie Xu

  • Mobile Cloud Computing: A Survey, State of Art and Future Directions

    M. Reza Rahimi;Jian Ren;Chi Harold Liu;Athanasios V. Vasilakos

  • Experience-driven Networking: A Deep Reinforcement Learning based Approach

    Zhiyuan Xu;Jian Tang;Jingsong Meng;Weiyi Zhang

  • Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning

    Chi Harold Liu;Qiuxia Lin;Shilin Wen

  • Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning

    Chi Harold Liu;Xiaoxin Ma;Xudong Gao;Jian Tang

  • Context-Awareness for Mobile Sensing: A Survey and Future Directions

    Ozgur Yurur;Chi Harold Liu;Zhengguo Sheng;Victor C. M. Leung

  • Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

    Charith Perera;Arkady Zaslavsky;Chi Harold Liu;Michael Compton

  • A Survey of Incentive Mechanisms for Participatory Sensing

    Hui Gao;Chi Harold Liu;Wendong Wang;Jianxin Zhao

  • Efficient naming, addressing and profile services in Internet-of-Things sensory environments

    Chi Harold Liu;Bo Yang;Tiancheng Liu

  • MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

    Shuang Li;Kaixiong Gong;Chi Harold Liu;Yulin Wang

  • QoI-Aware Multitask-Oriented Dynamic Participant Selection With Budget Constraints

    Zheng Song;Chi Harold Liu;Jie Wu;Jian Ma

  • Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities

    Bo Zhang;Chi Harold Liu;Jian Tang;Zhiyuan Xu

  • Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation

    Tong Wu;Junshi Huang;Guangyu Gao;Xiaoming Wei

  • USA: Faster update for SDN-based internet of things sensory environments

    Tao Liu;Chi Harold Liu;Chi Harold Liu;Wendong Wang;Xiangyang Gong

  • Deep Residual Correction Network for Partial Domain Adaptation

    Shuang Li;Chi Harold Liu;Qiuxia Lin;Qi Wen

  • Heterogeneous Multi-Task Assignment in Mobile Crowdsensing Using Spatiotemporal Correlation

    Liang Wang;Zhiwen Yu;Daqing Zhang;Bin Guo

  • Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach

    Chi Harold Liu;Zheyu Chen;Yufeng Zhan

  • Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning

    Chi Harold Liu;Zipeng Dai;Yinuo Zhao;Jon Crowcroft

  • Energy-Aware Participant Selection for Smartphone-Enabled Mobile Crowd Sensing

    Chi Harold Liu;Bo Zhang;Xin Su;Jian Ma

  • Joint Adversarial Domain Adaptation

    Shuang Li;Chi Harold Liu;Binhui Xie;Limin Su

Frequent Co-Authors

Kin K. Leung
Kin K. Leung Imperial College London
Jian Tang
Jian Tang Syracuse University
Wendong Wang
Wendong Wang University of Science and Technology of China
Charith Perera
Charith Perera Cardiff University
Jun Fan
Jun Fan Missouri University of Science and Technology
Zhengming Ding
Zhengming Ding Tulane University
Jian Ma
Jian Ma City University of Hong Kong
Pan Hui
Pan Hui Hong Kong University of Science and Technology
Jon Crowcroft
Jon Crowcroft University of Cambridge
Zhengguo Sheng
Zhengguo Sheng University of Sussex

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