World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
8537
World Ranking
5681
National Ranking
751

Chuan Wu 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 Chuan Wu 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 178 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Chuan Wu 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 Chuan Wu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Chuan Wu is affiliated with the University of Hong Kong in China and has contributed extensively to research in the fields of Engineering and Computer Science. Their work spans multiple publications, with a particular focus on Mechanical Engineering and Artificial Intelligence, among other specialized subfields.

Their scholarly output includes papers covering materials science topics such as high entropy alloys and additive manufacturing processes. Notable recent publications include:

  • Laser surface alloying of FeCoCrAlNiTi high entropy alloy composite coatings reinforced with TiC on 304 stainless steel to enhance wear behavior, 2022, Ceramics International
  • Tribological properties and sulfuric acid corrosion resistance of laser clad CoCrFeNi high entropy alloy coatings with different types of TiC reinforcement, 2023, Tribology International
  • Exploration of wear and slurry erosion mechanisms of laser clad CoCrFeNi + x (NbC) high entropy alloys composite coatings, 2024, Tribology International

Wu's research frequently appears in highly specialized venues such as:

  • arXiv (Cornell University)
  • Surface and Coatings Technology
  • IEEE Transactions on Parallel and Distributed Systems
  • Journal of Materials Engineering and Performance
  • Materials Characterization

Their work addresses a range of main topics including:

  • High Entropy Alloys Studies
  • Additive Manufacturing Materials and Processes
  • High-Temperature Coating Behaviors
  • Advanced Graph Neural Networks
  • IoT and Edge/Fog Computing
  • Cloud Computing and Resource Management
  • Stochastic Gradient Optimization Techniques

Frequent coauthors collaborating with Wu include Song Zhang, C.H. Zhang, H.T. Chen, C. H. Zhang, and Tingting Xu, indicating active engagement in multi-author interdisciplinary research teams.

Their research contributes both to experimental materials engineering-especially regarding surface treatments and alloy coatings-and to computational methods such as deep learning cluster scheduling and advanced networking within computer science.

Best Publications

  • An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing

    Weijie Shi;Linquan Zhang;Chuan Wu;Zongpeng Li

  • Optimus: an efficient dynamic resource scheduler for deep learning clusters

    Yanghua Peng;Yixin Bao;Yangrui Chen;Chuan Wu

  • A generic communication scheduler for distributed DNN training acceleration

    Yanghua Peng;Yibo Zhu;Yangrui Chen;Yixin Bao

  • Online Auctions in IaaS Clouds: Welfare and Profit Maximization With Server Costs

    Xiaoxi Zhang;Zhiyi Huang;Chuan Wu;Zongpeng Li

  • UUSee: Large-Scale Operational On-Demand Streaming with Random Network Coding

    Zimu Liu;Chuan Wu;Baochun Li;Shuqiao Zhao

  • Dynamic resource provisioning in cloud computing: A randomized auction approach

    Linquan Zhang;Zongpeng Li;Chuan Wu

  • Scaling social media applications into geo-distributed clouds

    Yu Wu;Chuan Wu;Bo Li;Linquan Zhang

  • DAPPLE: a pipelined data parallel approach for training large models

    Shiqing Fan;Yi Rong;Chen Meng;Zongyan Cao

  • Multi-Channel Live P2P Streaming: Refocusing on Servers

    Chuan Wu;Baochun Li;Shuqiao Zhao

  • Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybrid Clouds

    Xuanjia Qiu;Hongxing Li;Chuan Wu;Zongpeng Li

  • CloudMedia: When Cloud on Demand Meets Video on Demand

    Yu Wu;Chuan Wu;Bo Li;Xuanjia Qiu

  • Moving Big Data to The Cloud: An Online Cost-Minimizing Approach

    Linquan Zhang;Chuan Wu;Zongpeng Li;Chuanxiong Guo

  • Deep Learning-based Job Placement in Distributed Machine Learning Clusters

    Yixin Bao;Yanghua Peng;Chuan Wu

  • A survey on cloud interoperability: taxonomies, standards, and practice

    Zhizhong Zhang;Chuan Wu;David W.L. Cheung

  • Strategyproof auctions for balancing social welfare and fairness in secondary spectrum markets

    Ajay Gopinathan;Zongpeng Li;Chuan Wu

  • Online Scaling of NFV Service Chains Across Geo-Distributed Datacenters

    Yongzheng Jia;Chuan Wu;Zongpeng Li;Franck Le

  • Dynamic pricing and profit maximization for the cloud with geo-distributed data centers

    Jian Zhao;Hongxing Li;Chuan Wu;Zongpeng Li

  • Orchestrating Bulk Data Transfers across Geo-Distributed Datacenters

    Yu Wu;Zhizhong Zhang;Chuan Wu;Chuanxiong Guo

  • Online Job Scheduling in Distributed Machine Learning Clusters

    Yixin Bao;Yanghua Peng;Chuan Wu;Zongpeng Li

  • Exploring large-scale peer-to-peer live streaming topologies

    Chuan Wu;Baochun Li;Shuqiao Zhao

Frequent Co-Authors

Zongpeng Li
Zongpeng Li Tsinghua University
Francis C. M. Lau
Francis C. M. Lau Hong Kong Polytechnic University
Baochun Li
Baochun Li University of Toronto
Shaolei Ren
Shaolei Ren University of California, Riverside
Shiqiang Yang
Shiqiang Yang Tsinghua University
Minghua Chen
Minghua Chen City University of Hong Kong
Alex X. Liu
Alex X. Liu Michigan State University
Xiaojun Lin
Xiaojun Lin Purdue University West Lafayette
Wenwu Zhu
Wenwu Zhu Tsinghua University

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