World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
6837
World Ranking
8448
National Ranking
1100

Cheng 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 Cheng 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: 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: 214 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Cheng 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 Cheng Wu 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Cheng Wu is affiliated with Tsinghua University in China, where their research primarily spans the fields of Engineering and Computer Science. Their work encompasses subfields such as Artificial Intelligence, Mechanical Engineering, Control and Systems Engineering, Surgery, and Computer Vision and Pattern Recognition.

The scientist's research covers a variety of topics, including:

  • Reinforcement Learning in Robotics
  • Explainable Artificial Intelligence (XAI)
  • Advanced Graph Neural Networks
  • Domain Adaptation and Few-Shot Learning
  • Recommender Systems and Techniques
  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems

Cheng Wu has contributed to several publication venues, with a notable presence in:

  • arXiv (Cornell University)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • SSRN Electronic Journal
  • Neurocomputing
  • Digital Signal Processing

Frequent collaborators include Gao Huang, Shiji Song, Yulin Wang, Chongdang Liu, and Linxuan Zhang.

Selected recent publications by Cheng Wu include:

  • "Regularizing Deep Networks with Semantic Data Augmentation," 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism," 2020, Neurocomputing
  • "Itaconic acid-based hyperbranched polymer toughened epoxy resins with rapid stress relaxation, superb solvent resistance and closed-loop recyclability," 2022, Green Chemistry
  • "Closed-Loop Recycling of Tough and Flame-Retardant Epoxy Resins," 2023, Macromolecules
  • "Self-Supervised Discovering of Interpretable Features for Reinforcement Learning," 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence

Best Publications

  • Semi-Supervised and Unsupervised Extreme Learning Machines

    Gao Huang;Shiji Song;Jatinder N. D. Gupta;Cheng Wu

  • Carbon-efficient scheduling of flow shops by multi-objective optimization

    Jian-Ya Ding;Shiji Song;Cheng Wu

  • Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation

    Shuang Li;Shiji Song;Gao Huang;Zhengming Ding

  • Parallel Machine Scheduling Under Time-of-Use Electricity Prices: New Models and Optimization Approaches

    Jian-Ya Ding;Shiji Song;Rui Zhang;Raymond Chiong

  • A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.

    Tianyu Liu;Wenhui Fan;Cheng Wu

  • Reduction method for concept lattices based on rough set theory and its application

    Min Liu;Mingwen Shao;Wenxiu Zhang;Cheng Wu

  • Regularizing Deep Networks with Semantic Data Augmentation.

    Yulin Wang;Gao Huang;Shiji Song;Xuran Pan

  • Depth Control of Model-Free AUVs via Reinforcement Learning

    Hui Wu;Shiji Song;Keyou You;Cheng Wu

  • An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

    Jian-Ya Ding;Shiji Song;Jatinder N.D. Gupta;Rui Zhang

  • A hybrid artificial bee colony algorithm for the job shop scheduling problem

    Rui Zhang;Shiji Song;Cheng Wu

  • A hybrid immune simulated annealing algorithm for the job shop scheduling problem

    Rui Zhang;Cheng Wu

  • Efficient composite heuristics for total flowtime minimization in permutation flow shops

    Xiaoping Li;Qian Wang;Cheng Wu

  • Distributed Convex Optimization with Inequality Constraints over Time-Varying Unbalanced Digraphs

    Pei Xie;Keyou You;Roberto Tempo;Shiji Song

  • Domain Space Transfer Extreme Learning Machine for Domain Adaptation

    Yiming Chen;Shiji Song;Shuang Li;Le Yang

  • A simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardinessobjective

    Rui Zhang;Cheng Wu

  • Impact of loss aversion on the newsvendor game with product substitution

    Wei Liu;Shiji Song;Cheng Wu

  • A Graph Embedding Framework for Maximum Mean Discrepancy-Based Domain Adaptation Algorithms

    Yiming Chen;Shiji Song;Shuang Li;Cheng Wu

  • Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

    Wenjie Shi;Shiji Song;Cheng Wu;C. L. Philip Chen

  • Supply chain coordination of loss-averse newsvendor with contract

    Long Zhang;Shiji Song;Cheng Wu

  • Implicit Semantic Data Augmentation for Deep Networks

    Yulin Wang;Xuran Pan;Shiji Song;Hong Zhang

  • Heuristic for no-wait flow shops with makespan minimization

    Xiaoping Li;Qian Wang;Cheng Wu

  • Implicit Semantic Data Augmentation for Deep Networks

    Yulin Wang;Xuran Pan;Shiji Song;Hong Zhang

Frequent Co-Authors

Shiji Song
Shiji Song Tsinghua University
Keyou You
Keyou You Tsinghua University
Rui Zhang
Rui Zhang National University of Singapore
Gao Huang
Gao Huang Tsinghua University
Fan Zhang
Fan Zhang Chinese Academy of Sciences
Ke Xu
Ke Xu Tsinghua University
Jatinder N. D. Gupta
Jatinder N. D. Gupta University of Alabama in Huntsville
Pei-Chann Chang
Pei-Chann Chang Yuan Ze University
Degang Chen
Degang Chen North China Electric Power University
Wei Tan
Wei Tan Citadel LLC

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