World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
12641
World Ranking
5271
National Ranking
2428

Ji 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 Ji 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: 199 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Ji 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 Ji 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Ji Liu is affiliated with Facebook in the United States and has a research portfolio focused predominantly in computer science, with a strong emphasis on artificial intelligence and computer vision. Their scholarly activity spans over 130 publications, addressing various subfields and applied topics within these domains.

The main areas of study for Ji Liu include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Physiology
  • Endocrine and Autonomic Systems

Within these subfields, Ji Liu's specific research interests comprise:

  • Advanced Neural Network Applications
  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data
  • Machine Learning and Data Classification
  • Generative Adversarial Networks and Image Synthesis
  • Adversarial Robustness in Machine Learning
  • Video Surveillance and Tracking Methods

Ji Liu has published frequently in several venues, reflecting the interdisciplinary nature of their research. The primary publication outlets include:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • American Journal of Physiology-Endocrinology and Metabolism
  • IEEE Internet of Things Journal

Some notable recent papers authored by Ji Liu are:

  • "Data Poisoning Attacks on Federated Machine Learning" (2021), published in IEEE Internet of Things Journal
  • "Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification" (2022), featured at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Activation of arcuate nucleus glucagon-like peptide-1 receptor-expressing neurons suppresses food intake" (2022), published in Cell & Bioscience
  • "Glucose-sensing glucagon-like peptide-1 receptor neurons in the dorsomedial hypothalamus regulate glucose metabolism" (2022), published in Science Advances
  • "Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters" (2022), published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Ji Liu frequently collaborates with a core group of coauthors, including:

  • Xiangru Lian
  • Binhang Yuan
  • Sen Yang
  • Shaoduo Gan
  • Zhaohuan Huang

Best Publications

  • Tensor completion for estimating missing values in visual data

    Ji Liu;Przemyslaw Musialski;Peter Wonka;Jieping Ye

  • Sparse reconstruction cost for abnormal event detection

    Yang Cong;Junsong Yuan;Ji Liu

  • Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

    Xiangru Lian;Ce Zhang;Huan Zhang;Cho-Jui Hsieh

  • Asynchronous parallel stochastic gradient for nonconvex optimization

    Xiangru Lian;Yijun Huang;Yuncheng Li;Ji Liu

  • IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

    Hui Chen;Guiguang Ding;Xudong Liu;Zijia Lin

  • Gradient Sparsification for Communication-Efficient Distributed Optimization

    Jianqiao Wangni;Jialei Wang;Ji Liu;Tong Zhang

  • Abnormal event detection in crowded scenes using sparse representation

    Yang Cong;Junsong Yuan;Ji Liu

  • An asynchronous parallel stochastic coordinate descent algorithm

    Ji Liu;Stephen J. Wright;Christopher Ré;Victor Bittorf

  • Asynchronous Decentralized Parallel Stochastic Gradient Descent

    Xiangru Lian;Wei Zhang;Ce Zhang;Ji Liu

  • Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties

    Ji Liu;Stephen J. Wright

  • $D^2$: Decentralized Training over Decentralized Data

    Hanlin Tang;Xiangru Lian;Ming Yan;Ce Zhang

  • Data Poisoning Attacks on Federated Machine Learning

    Gan Sun;Yang Cong;Jiahua Dong;Qiang Wang

  • Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks

    Jianhui Chen;Ji Liu;Jieping Ye

  • Staleness-aware async-SGD for distributed deep learning

    Wei Zhang;Suyog Gupta;Xiangru Lian;Ji Liu

  • Communication Compression for Decentralized Training

    Hanlin Tang;Shaoduo Gan;Ce Zhang;Tong Zhang

  • ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning

    Hantian Zhang;Jerry Li;Kaan Kara;Dan Alistarh

  • ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

    Xiaohan Ding;Tianxiang Hao;Jianchao Tan;Ji Liu

  • An accelerated randomized Kaczmarz algorithm

    Ji Liu;Stephen J. Wright

  • Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

    Xiaohan Ding;guiguang ding;Xiangxin Zhou;Yuchen Guo

  • DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression

    Hanlin Tang;Chen Yu;Xiangru Lian;Tong Zhang

  • DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression

    Hanlin Tang;Xiangru Lian;Chen Yu;Tong Zhang

  • LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning

    Yali Du;Lei Han;Meng Fang;Ji Liu

  • Data Poisoning Attacks on Federated Machine Learning

    Gan Sun;Yang Cong;Jiahua Dong;Qiang Wang

Frequent Co-Authors

Ce Zhang
Ce Zhang ETH Zurich
Yang Cong
Yang Cong Chinese Academy of Sciences
Zhangyang Wang
Zhangyang Wang The University of Texas at Austin
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Sridhar Mahadevan
Sridhar Mahadevan University of Massachusetts Amherst
Guiguang Ding
Guiguang Ding Tsinghua University
Jungong Han
Jungong Han Aberystwyth University
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Dacheng Tao
Dacheng Tao Nanyang Technological University
Jiebo Luo
Jiebo Luo University of Rochester

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