World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
5857
World Ranking
10268
National Ranking
642

Weisi Guo 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 Weisi Guo 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: 329 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Weisi Guo 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 Weisi Guo 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Weisi Guo is affiliated with Cranfield University in the United Kingdom. Their research spans multiple fields including Engineering and Computer Science, with a focus on several subfields such as Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Biomedical Engineering, and Aerospace Engineering.

The scientist has contributed to main research topics including Molecular Communication and Nanonetworks, Advanced Biosensing and Bioanalysis Techniques, Adaptive Dynamic Programming Control, Reinforcement Learning in Robotics, Wireless Body Area Networks, Adversarial Robustness in Machine Learning, and Complex Network Analysis Techniques.

Weisi Guo's recent scholarly articles include:

  • Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine (2020, IEEE Communications Magazine)
  • A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning (2020, IEEE Access)
  • Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms (2020, Signal Processing)
  • Trustworthy Deep Learning in 6G-Enabled Mass Autonomy: From Concept to Quality-of-Trust Key Performance Indicators (2020, IEEE Vehicular Technology Magazine)
  • Deep Reinforcement Learning for Optimal Hydropower Reservoir Operation (2021, Journal of Water Resources Planning and Management)

The scientist frequently publishes in venues such as arXiv (Cornell University), IEEE Transactions on Molecular Biological and Multi-Scale Communications, IEEE Communications Magazine, IEEE Access, and Scientific Reports.

Co-authorship collaborations are notable with researchers including Zhuangkun Wei, Adolfo Perrusquía, Antonios Tsourdos, Bin Li, and Mengbang Zou.

Weisi Guo has also contributed to book publications, notably the volume titled Bio-inspired Information and Communication Technologies, published in 2020 by the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Best Publications

  • A Comprehensive Survey of Recent Advancements in Molecular Communication

    Nariman Farsad;H. Birkan Yilmaz;Andrew Eckford;Chan-Byoung Chae

  • Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum

    Haijun Zhang;Xiaoli Chu;Weisi Guo;Siyi Wang

  • Tabletop molecular communication: text messages through chemical signals.

    Nariman Farsad;Weisi Guo;Andrew W. Eckford

  • Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine

    Weisi Guo

  • Heterogeneous Cellular Networks: Theory, Simulation and Deployment

    Xiaoli Chu;David Lopez-Perez;Yang Yang;Fredrik Gunnarsson

  • Molecular communications: channel model and physical layer techniques

    Weisi Guo;Taufiq Asyhari;Nariman Farsad;H. Birkan Yilmaz

  • Relay Deployment in Cellular Networks: Planning and Optimization

    Weisi Guo;T. O'Farrell

  • Automated small-cell deployment for heterogeneous cellular networks

    Weisi Guo;Siyi Wang;Xiaoli Chu;Jie Zhang

  • Molecular Versus Electromagnetic Wave Propagation Loss in Macro-Scale Environments

    Weisi Guo;Christos Mias;Nariman Farsad;Jiang-Lun Wu

  • A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

    Bo Ma;Weisi Guo;Jie Zhang

  • Device-to-device meets LTE-unlicensed

    Yue Wu;Weisi Guo;Hu Yuan;Long Li

  • Three-dimensional SOlar RAdiation Model (SORAM) and its application to 3-D urban planning

    Róbert Erdélyi;Yimin Wang;Weisi Guo;Edward Hanna

  • Performance analysis of micro unmanned airborne communication relays for cellular networks

    Weisi Guo;Conor Devine;Siyi Wang

  • RACH Preamble Repetition in NB-IoT Network

    Nan Jiang;Yansha Deng;Massimo Condoluci;Weisi Guo

  • Simultaneous Information and Energy Flow for IoT Relay Systems with Crowd Harvesting

    Weisi Guo;Sheng Zhou;Yunfei Chen;Siyi Wang

  • Deep learning methods for solving linear inverse problems: Research directions and paradigms

    Yanna Bai;Wei Chen;Jie Chen;Weisi Guo;Weisi Guo

  • Dynamic Cell Expansion with Self-Organizing Cooperation

    Weisi Guo;T. O'Farrell

  • Trustworthy Deep Learning in 6G-Enabled Mass Autonomy: From Concept to Quality-of-Trust Key Performance Indicators

    Chen Li;Weisi Guo;Schyler Chengyao Sun;Saba Al-Rubaye

  • Local Convexity Inspired Low-Complexity Noncoherent Signal Detector for Nanoscale Molecular Communications

    Bin Li;Mengwei Sun;Siyi Wang;Weisi Guo

  • Stable Distributions as Noise Models for Molecular Communication

    Nariman Farsad;Weisi Guo;Chan-Byoung Chae;Andrew Eckford

  • Transposition Errors in Diffusion-Based Mobile Molecular Communication

    Werner Haselmayr;Syed Muhammad Haider Aejaz;A. Taufiq Asyhari;Andreas Springer

  • Green cellular network: Deployment solutions, sensitivity and tradeoffs

    Weisi Guo;Tim O'Farrell

  • Google Trends can improve surveillance of Type 2 diabetes.

    Nataliya Tkachenko;Sarunkorn Chotvijit;Neha Gupta;Emma Bradley

  • Learning-Based Spectrum Sharing and Spatial Reuse in mm-Wave Ultradense Networks

    Chaoqiong Fan;Bin Li;Chenglin Zhao;Weisi Guo

  • Analyzing Large-Scale Multiuser Molecular Communication via 3D Stochastic Geometry

    Yansha Deng;Adam Noel;Weisi Guo;Arumugam Nallanathan

Frequent Co-Authors

Andrew W. Eckford
Andrew W. Eckford York University
Chan-Byoung Chae
Chan-Byoung Chae Yonsei University
Xiaoli Chu
Xiaoli Chu University of Sheffield
Arumugam Nallanathan
Arumugam Nallanathan Queen Mary University of London
Yansha Deng
Yansha Deng King's College London
Jie Zhang
Jie Zhang East China University of Science and Technology
Stephen A. Jarvis
Stephen A. Jarvis University of Birmingham
Maged Elkashlan
Maged Elkashlan Queen Mary University of London
Guangtao Fu
Guangtao Fu University of Exeter
Andrea Goldsmith
Andrea Goldsmith Stony Brook University

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