World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
27663
World Ranking
1832
National Ranking
933

Wei Wang 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 Wei Wang 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: 417 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Wei Wang 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 Wei Wang 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: 70 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Wei Wang is affiliated with the University of California, Los Angeles in the United States. Their research portfolio spans Engineering and Computer Science, with 300 publications in Engineering and 171 in Computer Science. The subfields of study include Control and Systems Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Computer Networks and Communications, and Mechanical Engineering.

The scientist's work focuses on several main topics:

  • Microgrid Control and Optimization
  • Distributed Control Multi-Agent Systems
  • Neural Networks Stability and Synchronization
  • Fault Detection and Control Systems
  • Power Systems and Renewable Energy
  • Smart Grid Energy Management
  • Neural Networks and Applications

Wei Wang has collaborated frequently with several coauthors, including Jun Zhao, Dong Wang, Quanli Liu, Long Chen, and Zhongyang Han. These collaborations have resulted in multiple scholarly outputs.

Their research outputs have appeared in various academic venues. The most frequent publication outlets are:

  • arXiv (Cornell University)
  • IEEE Transactions on Instrumentation and Measurement
  • SSRN Electronic Journal
  • IEEE Transactions on Industrial Electronics
  • Information Sciences

Recent papers by Wei Wang include:

  • Hierarchical stability conditions for time-varying delay systems via an extended reciprocally convex quadratic inequality, 2020, Journal of the Franklin Institute
  • Design of Hybrid Event-Triggered Containment Controllers for Homogeneous and Heterogeneous Multiagent Systems, 2020, IEEE Transactions on Cybernetics
  • Multivariate time-series classification with hierarchical variational graph pooling, 2022, Neural Networks
  • Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Hierarchical optimal configuration of multi-energy microgrids system considering energy management in electricity market environment, 2022, International Journal of Electrical Power & Energy Systems

Best Publications

  • A second generation human haplotype map of over 3.1 million SNPs

    Kelly A. Frazer;Dennis G. Ballinger;David R. Cox;David A. Hinds

  • STING: A Statistical Information Grid Approach to Spatial Data Mining

    Wei Wang;Jiong Yang;Richard R. Muntz

  • Efficient mining of frequent subgraphs in the presence of isomorphism

    J. Huan;W. Wang;J. Prins

  • Fast computation of database operations using graphics processors

    Naga K. Govindaraju;Brandon Lloyd;Wei Wang;Ming Lin

  • End-to-end encrypted traffic classification with one-dimensional convolution neural networks

    Wei Wang;Ming Zhu;Jinlin Wang;Xuewen Zeng

  • Clustering by pattern similarity in large data sets

    Haixun Wang;Wei Wang;Jiong Yang;Philip S. Yu

  • HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

    Wei Wang;Yiqiang Sheng;Jinlin Wang;Xuewen Zeng

  • Ranking outliers using symmetric neighborhood relationship

    Wen Jin;Anthony K. H. Tung;Jiawei Han;Wei Wang

  • SPIN: mining maximal frequent subgraphs from graph databases

    Jun Huan;Wei Wang;Jan Prins;Jiong Yang

  • /spl delta/-clusters: capturing subspace correlation in a large data set

    Jiong Yang;Wei Wang;Haixun Wang;P. Yu

  • Enhanced biclustering on expression data

    Jiong Yang;Haixun Wang;Wei Wang;P. Yu

  • Genetic analysis of complex traits in the emerging Collaborative Cross

    David L. Aylor;William Valdar;Wendy Foulds-Mathes;Ryan J. Buus

  • Learning Gender-Neutral Word Embeddings

    Jieyu Zhao;Yichao Zhou;Zeyu Li;Wei Wang

  • Efficient mining of weighted association rules (WAR)

    Wei Wang;Jiong Yang;Philip S. Yu

  • Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

    Muhao Chen;Chelsea J T Ju;Guangyu Zhou;Xuelu Chen

  • NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks

    Wenchao Yu;Wei Cheng;Charu C. Aggarwal;Kai Zhang

  • Machine Learning and Integrative Analysis of Biomedical Big Data.

    Bilal Mirza;Wei Wang;Jie Wang;Howard Choi

  • SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

    Yunsheng Bai;Hao Ding;Song Bian;Ting Chen

  • Mining asynchronous periodic patterns in time series data

    Jiong Yang;Wei Wang;P.S. Yu

  • Distributed sparse random projections for refinable approximation

    Wei Wang;Minos Garofalakis;Kannan Ramchandran

  • Graph Database Indexing Using Structured Graph Decomposition

    D. W. Williams;Jun Huan;Wei Wang

  • OP-cluster: clustering by tendency in high dimensional space

    J. Liu;W. Wang

Frequent Co-Authors

Xiang Zhang
Xiang Zhang University of Hong Kong
Yizhou Sun
Yizhou Sun University of California, Los Angeles
Leonard McMillan
Leonard McMillan University of North Carolina at Chapel Hill
Peipei Ping
Peipei Ping University of California, Los Angeles
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Jan F. Prins
Jan F. Prins University of North Carolina at Chapel Hill
David W. Threadgill
David W. Threadgill Texas A&M University
Fernando Pardo-Manuel de Villena
Fernando Pardo-Manuel de Villena University of North Carolina at Chapel Hill
Richard R. Muntz
Richard R. Muntz University of California, Los Angeles
Muhao Chen
Muhao Chen University of California, Los Angeles

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