World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8204
World Ranking
10543
National Ranking
4420

Wei Ding 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 Ding 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.

Wei Ding 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 Ding 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Wei Ding is affiliated with the University of Massachusetts Boston in the United States. Their research primarily spans the fields of computer science and engineering, with a substantial focus on artificial intelligence, computer vision and pattern recognition, signal processing, control and systems engineering, and molecular biology.

The main research topics covered by Wei Ding include anomaly detection techniques and applications, time series analysis and forecasting, music and audio processing, air quality monitoring and forecasting, IoT and edge/fog computing, model reduction, neural networks, machine learning, and data classification.

Wei Ding has published extensively in various venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • ACM Transactions on Knowledge Discovery from Data
  • IEEE Intelligent Systems
  • IEEE Access

Recent papers authored or coauthored by Wei Ding illustrate a diverse range of interests and venues, including:

  • "CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing," 2021, arXiv (Cornell University)
  • "Causal Feature Selection with Missing Data," 2022, ACM Transactions on Knowledge Discovery from Data
  • "Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics," 2023, Journal of Chemical Information and Modeling
  • "Edge-cloud Collaboration-driven Predictive Planning Based on LSTM-attention for Wastewater Treatment," 2024, Computers & Industrial Engineering
  • "Introducing Time Series Snippets: A New Primitive for Summarizing Long Time Series," 2020, Data Mining and Knowledge Discovery

Frequent coauthors collaborating with Wei Ding include:

  • Ping Chen
  • Scott E. Crouter
  • Matthew Almeida
  • Zihan Li
  • Tianyu Kang

Best Publications

  • Data mining with big data

    Xindong Wu;Xingquan Zhu;Gong-Qing Wu;Wei Ding

  • Online Feature Selection with Streaming Features

    Xindong Wu;Kui Yu;Wei Ding;Hao Wang

  • Crime Forecasting Using Data Mining Techniques

    Chung-Hsien Yu;Max W. Ward;Melissa Morabito;Wei Ding

  • Enhancement of Immunoassay’s Fluorescence and Detection Sensitivity Using Three-Dimensional Plasmonic Nano-Antenna-Dots Array

    Liangcheng Zhou;Fei Ding;Hao Chen;Wei Ding

  • Online Streaming Feature Selection

    Xindong Wu;Xindong Wu;Kui Yu;Hao Wang;Wei Ding

  • Scalable and Accurate Online Feature Selection for Big Data

    Kui Yu;Xindong Wu;Wei Ding;Jian Pei

  • Learning a Proposal Classifier for Multiple Object Tracking

    Peng Dai;Renliang Weng;Wongun Choi;Changshui Zhang

  • Towards Scalable and Accurate Online Feature Selection for Big Data

    Kui Yu;Xindong Wu;Wei Ding;Jian Pei

  • Crime hotspot mapping using the crime related factors--a spatial data mining approach

    Dawei Wang;Wei Ding;Henry Lo;Tomasz Stepinski

  • Multi-Source Causal Feature Selection

    Kui Yu;Lin Liu;Jiuyong Li;Wei Ding

  • Detection of Sub-Kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features

    Louren » co Bandeira;Wei Ding;Tomasz F. Stepinski

  • Subkilometer crater discovery with boosting and transfer learning

    Wei Ding;Tomasz F. Stepinski;Yang Mu;Lourenco Bandeira

  • Finding regional co-location patterns for sets of continuous variables in spatial datasets

    Christoph F. Eick;Rachana Parmar;Wei Ding;Tomasz F. Stepinski

  • Authorship identification from unstructured texts

    Chunxia Zhang;Xindong Wu;Zhendong Niu;Wei Ding

  • Designing efficient accelerator of depthwise separable convolutional neural network on FPGA

    Unknown

  • Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns

    Dawei Wang;Wei Ding;Henry Z. Lo;Melissa Morabito

  • Mining sequential patterns with periodic wildcard gaps

    Youxi Wu;Lingling Wang;Jiadong Ren;Wei Ding

  • Using a model checker to test safety properties

    P. Ammann;Wei Ding;Daling Xu

  • Local discriminative distance metrics ensemble learning

    Yang Mu;Wei Ding;Dacheng Tao

  • A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge

    Ping Chen;Wei Ding;Chris Bowes;David Brown

  • Online Learning from Trapezoidal Data Streams

    Qin Zhang;Peng Zhang;Guodong Long;Wei Ding

  • Model Checkers in Software Testing

    Paul E Black;Paul Ammann;Wei Ding

  • Evaluation of three specification-based testing criteria

    A. Abdurazik;P. Ammann;Wei Ding;J. Offutt

  • Cancer subtype identification using somatic mutation data.

    Marieke Lydia Kuijjer;Joseph Nathaniel Paulson;Joseph Nathaniel Paulson;Peter Salzman;Wei Ding

Frequent Co-Authors

Stephen Y. Chou
Stephen Y. Chou Princeton University
Xindong Wu
Xindong Wu Hefei University of Technology
Ru Huang
Ru Huang Peking University
John Quackenbush
John Quackenbush Harvard University
Jian Pei
Jian Pei Duke University
Dacheng Tao
Dacheng Tao Nanyang Technological University
Shafiqul Islam
Shafiqul Islam Tufts University
Yangyuan Wang
Yangyuan Wang Peking University
Xingquan Zhu
Xingquan Zhu Florida Atlantic University
Eamonn Keogh
Eamonn Keogh University of California, Riverside

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