World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
11533
World Ranking
6728
National Ranking
2969

Victor S. Sheng 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 Victor S. Sheng 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: 229 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Victor S. Sheng 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 Victor S. Sheng 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: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Overview

Victor S. Sheng is affiliated with Texas Tech University in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence.

The scientist's work covers several subfields within Computer Science, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications

The main research topics addressed in their publications include:

  • Recommender Systems and Techniques
  • Topic Modeling
  • Advanced Graph Neural Networks
  • Advanced Neural Network Applications
  • Network Security and Intrusion Detection
  • Text and Document Classification Technologies
  • Mobile Crowdsensing and Crowdsourcing

Victor S. Sheng has contributed frequently to notable publication venues, among them:

  • arXiv (Cornell University)
  • Expert Systems with Applications
  • Computers, materials & continua
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering

Recent papers authored or coauthored by Sheng include:

  • "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation" (2020), published in IEEE Transactions on Knowledge and Data Engineering
  • "Deep semi-supervised learning for medical image segmentation: A review" (2024), published in Expert Systems with Applications
  • "Long- and short-term self-attention network for sequential recommendation" (2020), published in Neurocomputing
  • "Multi-Label Active Learning Algorithms for Image Classification" (2020), published in ACM Computing Surveys
  • "Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges" (2020), published in IEEE Transactions on Emerging Topics in Computational Intelligence

Frequent collaborators in Sheng's work include:

  • Pengpeng Zhao
  • Wei Fang
  • Guanfeng Liu
  • Jieren Cheng
  • Yanchi Liu

In addition to journal and conference publications, Victor S. Sheng has published a book titled "Big Data and Security" in 2022 through Springer Science+Business Media.

Best Publications

  • Get another label? improving data quality and data mining using multiple, noisy labelers

    Victor S. Sheng;Foster Provost;Panagiotis G. Ipeirotis

  • Incremental Support Vector Learning for Ordinal Regression

    Bin Gu;Victor S. Sheng;Keng Yeow Tay;Walter Romano

  • Graph contextualized self-attention network for session-based recommendation

    Chengfeng Xu;Pengpeng Zhao;Yanchi Liu;Victor S. Sheng

  • Incremental learning for ν -Support Vector Regression

    Bin Gu;Victor S. Sheng;Zhijie Wang;Derek Ho

  • A Robust Regularization Path Algorithm for $ u $ -Support Vector Classification

    Bin Gu;Victor S. Sheng

  • Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

    Pengpeng Zhao;Anjing Luo;Yanchi Liu;Fuzhen Zhuang

  • Structural Minimax Probability Machine

    Bin Gu;Xingming Sun;Victor S. Sheng

  • Feature-level Deeper Self-Attention Network for Sequential Recommendation

    Tingting Zhang;Pengpeng Zhao;Yanchi Liu;Victor S. Sheng

  • A Comparative Study of SIFT and its Variants

    Jian Wu;Zhiming Cui;Victor S. Sheng;Pengpeng Zhao

  • Cost-Sensitive Learning and the Class Imbalance Problem

    Charles X. Ling;Victor S. Sheng

  • Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

    Pengpeng Zhao;Haifeng Zhu;Yanchi Liu;Jiajie Xu

  • Repeated labeling using multiple noisy labelers

    Panagiotis G. Ipeirotis;Foster Provost;Victor S. Sheng;Jing Wang

  • A method for improving CNN-based image recognition using DCGAN

    Wei Fang;Wei Fang;Feihong Zhang;Victor S. Sheng;Yewen Ding

  • Thresholding for making classifiers cost-sensitive

    Victor S. Sheng;Charles X. Ling

  • Test strategies for cost-sensitive decision trees

    C.X. Ling;V.S. Sheng;Q. Yang

  • Learning from crowdsourced labeled data: a survey

    Jing Zhang;Xindong Wu;Victor S. Sheng

  • Liver CT sequence segmentation based with improved U-Net and graph cut

    Zhe Liu;Yu-Qing Song;Victor S. Sheng;Liangmin Wang

  • Cost-Sensitive Learning.

    Charles X. Ling;Victor S. Sheng

  • Recurrent Convolutional Neural Network for Sequential Recommendation

    Chengfeng Xu;Pengpeng Zhao;Yanchi Liu;Jiajie Xu

  • An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment

    Renjie Cheng;Ruomeng Xu;Xiangyan Tang;Victor S Sheng

Frequent Co-Authors

Jian Wu
Jian Wu Fudan University
Charles X. Ling
Charles X. Ling University of Western Ontario
Xindong Wu
Xindong Wu Hefei University of Technology
Fuzhen Zhuang
Fuzhen Zhuang Beihang University
Xiaofang Zhou
Xiaofang Zhou Hong Kong University of Science and Technology
Guanfeng Liu
Guanfeng Liu Macquarie University
An Liu
An Liu Soochow University
Panagiotis G. Ipeirotis
Panagiotis G. Ipeirotis New York University
Foster Provost
Foster Provost New York University
Hui Xiong
Hui Xiong Rutgers, The State University of New Jersey

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