World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
21260
World Ranking
1770
National Ranking
243

Min Zhang 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 Min Zhang 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: 633 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Min Zhang 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 Min Zhang 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: 71 D-Index — 88th percentile

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

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

Overview

Min Zhang is affiliated with Tsinghua University in China and has contributed extensively to research in computer science, engineering, and environmental science. Their work prominently intersects the domains of artificial intelligence, computer vision, and environmental engineering.

The main fields of study for Min Zhang include:

  • Computer Science
  • Engineering
  • Environmental Science

Their research encompasses several key subfields:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Environmental Engineering
  • Global and Planetary Change

Min Zhang has addressed a variety of topics, notably:

  • Remote-Sensing Image Classification
  • Landslides and related hazards
  • Topic Modeling
  • Natural Language Processing Techniques
  • Remote Sensing and Land Use
  • Flood Risk Assessment and Management
  • Advanced Image and Video Retrieval Techniques

Their recent papers reflect a strong focus on remote sensing and artificial intelligence applications in environmental monitoring and hazard analysis. These papers include:

  • A Feature Difference Convolutional Neural Network-Based Change Detection Method (2020, IEEE Transactions on Geoscience and Remote Sensing)
  • Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges (2020, Remote Sensing)
  • Landslide Recognition by Deep Convolutional Neural Network and Change Detection (2020, IEEE Transactions on Geoscience and Remote Sensing)
  • Application of power ultrasound in freezing and thawing Processes: Effect on process efficiency and product quality (2020, Ultrasonics Sonochemistry)
  • Environmental impact evaluation of an iron and steel plant in China: Normalized data and direct/indirect contribution (2020, Journal of Cleaner Production)

Min Zhang frequently collaborates with several researchers, including:

  • Wenzhong Shi
  • Lukang Wang
  • Meishan Zhang
  • Shanxiong Chen
  • Zhao Zhan

The scientist's publications appear in notable venues such as:

  • arXiv (Cornell University)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Remote Sensing
  • SSRN Electronic Journal
  • IEEE Transactions on Geoscience and Remote Sensing

This profile delineates a research career emphasizing AI-driven change detection, environmental monitoring, and the application of deep learning to geoscience challenges.

Best Publications

  • Exploring Various Knowledge in Relation Extraction

    GuoDong Zhou;Jian Su;Jie Zhang;Min Zhang

  • Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

    Yongfeng Zhang;Guokun Lai;Min Zhang;Yi Zhang

  • Neural Attentional Rating Regression with Review-level Explanations

    Chong Chen;Min Zhang;Yiqun Liu;Shaoping Ma

  • Reviewer bias in single- versus double-blind peer review

    Andrew Tomkins;Min Zhang;William D. Heavlin

  • LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding

    Yang Xu;Yiheng Xu;Tengchao Lv;Lei Cui

  • A Phrase-Based Statistical Model for SMS Text Normalization

    AiTi Aw;Min Zhang;Juan Xiao;Jian Su

  • A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features

    Min Zhang;Jie Zhang;Jian Su;GuoDong Zhou

  • R-Drop: Regularized Dropout for Neural Networks

    xiaobo liang;Lijun Wu;Juntao Li;Yue Wang

  • A Joint Source-Channel Model for Machine Transliteration

    Haizhou Li;Min Zhang;Jian Su

  • Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information

    GuoDong Zhou;Min Zhang;DongHong Ji;QiaoMing Zhu

  • Improving the Transformer Translation Model with Document-Level Context.

    Jiacheng Zhang;Huanbo Luan;Maosong Sun;Feifei Zhai

  • How good your recommender system is? A survey on evaluations in recommendation

    Thiago Silveira;Min Zhang;Xiao Lin;Yiqun Liu

  • Optimizing Dense Retrieval Model Training with Hard Negatives

    Jingtao Zhan;Jiaxin Mao;Yiqun Liu;Jiafeng Guo

  • Variational Neural Machine Translation

    Biao Zhang;Deyi Xiong;Jinsong Su;Hong Duan

  • Fast and Accurate Shift-Reduce Constituent Parsing

    Muhua Zhu;Yue Zhang;Wenliang Chen;Min Zhang

  • Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

    Weizhi Ma;Min Zhang;Yue Cao;Woojeong Jin

  • Efficient Neural Matrix Factorization without Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Yiqun Liu

  • Graph Heterogeneous Multi-Relational Recommendation

    Chong Chen;Weizhi Ma;Min Zhang;Zhaowei Wang

  • Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

    Chong Chen;Min Zhang;Yongfeng Zhang;Weizhi Ma

  • Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier

    Zhao Zhang;Weiming Jiang;Jie Qin;Li Zhang

  • Rating-boosted latent topics: understanding users and items with ratings and reviews

    Yunzhi Tan;Min Zhang;Yiqun Liu;Shaoping Ma

  • Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

    Maarten de Rijke;Milad Shokouhi;Andrew Tomkins;Min Zhang

  • Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

    Weizhi Ma;Min Zhang;Yue Cao;Woojeong

Frequent Co-Authors

Shaoping Ma
Shaoping Ma Tsinghua University
Haizhou Li
Haizhou Li Chinese University of Hong Kong, Shenzhen
Deyi Xiong
Deyi Xiong Tianjin University
Guodong Zhou
Guodong Zhou Soochow University
Yongfeng Zhang
Yongfeng Zhang Rutgers, The State University of New Jersey
Yue Zhang
Yue Zhang Westlake University
Chew Lim Tan
Chew Lim Tan National University of Singapore
Xiaohui Xie
Xiaohui Xie University of California, Irvine
Luo Si
Luo Si Alibaba Group (China)
Jinsong Su
Jinsong Su Xiamen University

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Related Online Degrees & Career Pathways

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Computer Science intersects with several fields, providing versatile career options. For example, those interested in sustainability might explore what what jobs can you get with an environmental science degree. Many tech skills are transferable, and roles in environmental data analysis or modeling are in high demand.

Engineering is another promising avenue. If you’re budget-conscious, there are several environmental engineering degree programs online that combine affordability with quality education. These interdisciplinary paths can enhance your employability in both technology and environmental sectors.

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