World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
43
Citations
6879
World Ranking
6213
National Ranking
1174

Bei Yu publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Bei Yu sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 134 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 117 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 59 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 216 publications — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Bei Yu D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Bei Yu sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 128 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 349 scientists 41 D-Index: 362 scientists 42 D-Index: 425 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 94 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 24 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 43 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Bei Yu is affiliated with the Chinese University of Hong Kong in China and has a substantial body of research primarily within computer science and engineering. Their work encompasses several subfields including electrical and electronic engineering, computer vision and pattern recognition, artificial intelligence, hardware and architecture, and industrial and manufacturing engineering.

Their research topics cover a diverse range of areas such as advancements in photolithography techniques, VLSI and FPGA design techniques, advanced neural network applications, industrial vision systems and defect detection, domain adaptation and few-shot learning, VLSI and analog circuit testing, as well as parallel computing and optimization techniques.

Bei Yu has published extensively, contributing to several frequent publication venues:

  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • arXiv (Cornell University)
  • ACM Transactions on Design Automation of Electronic Systems
  • 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
  • Proceedings of the AAAI Conference on Artificial Intelligence

Key recent papers authored or coauthored by Bei Yu include:

  • Machine Learning for Electronic Design Automation: A Survey, 2021, ACM Transactions on Design Automation of Electronic Systems
  • PCL: Proxy-based Contrastive Learning for Domain Generalization, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper, 2021, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration Framework, 2021, 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)

Frequent collaborators include Yuzhe Ma, Qi Sun, Tinghuan Chen, Martin D. F. Wong, and Tsung-Yi Ho, reflecting a collaborative approach across various aspects of computer-aided design and machine learning.

Best Publications

  • Recent advances in convolutional neural network acceleration

    Qianru Zhang;Meng Zhang;Tinghuan Chen;Tinghuan Chen;Zhifei Sun

  • Machine Learning for Electronic Design Automation: A Survey

    Guyue Huang;Jingbo Hu;Yifan He;Jialong Liu

  • Provably Secure Camouflaging Strategy for IC Protection

    Meng Li;Kaveh Shamsi;Travis Meade;Zheng Zhao

  • Layout Decomposition for Triple Patterning Lithography

    Bei Yu;Kun Yuan;Duo Ding;David Z. Pan

  • Self-Aligned Double Patterning Aware Pin Access and Standard Cell Layout Co-Optimization

    Xiaoqing Xu;Brian Cline;Greg Yeric;Bei Yu

  • Layout decomposition for triple patterning lithography

    Bei Yu;Kun Yuan;Boyang Zhang;Duo Ding

  • Optical proximity correction with hierarchical Bayes model

    Tetsuaki Matsunawa;Bei Yu;David Z. Pan

  • DeepBillboard: systematic physical-world testing of autonomous driving systems

    Husheng Zhou;Wei Li;Zelun Kong;Junfeng Guo

  • Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning

    Haoyu Yang;Jing Su;Yi Zou;Yuzhe Ma

  • GAN-OPC: Mask Optimization With Lithography-Guided Generative Adversarial Nets

    Haoyu Yang;Shuhe Li;Zihao Deng;Yuzhe Ma

  • PARR: Pin-Access Planning and Regular Routing for Self-Aligned Double Patterning

    Xiaoqing Xu;Bei Yu;Jhih-Rong Gao;Che-Lun Hsu

  • Imbalance aware lithography hotspot detection: a deep learning approach

    Haoyu Yang;Luyang Luo;Jing Su;Chenxi Lin

  • Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning

    Haoyu Yang;Jing Su;Yi Zou;Bei Yu

  • Design for Manufacturing With Emerging Nanolithography

    D. Z. Pan;Bei Yu;Jhih-Rong Gao

  • High Performance Graph ConvolutionaI Networks with Applications in Testability Analysis

    Yuzhe Ma;Haoxing Ren;Brucek Khailany;Harbinder Sikka

  • Enabling online learning in lithography hotspot detection with information-theoretic feature optimization

    Hang Zhang;Bei Yu;Evangeline F. Y. Young

  • Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment

    Ruixing Wang;Xiaogang Xu;Chi-Wing Fu;Jiangbo Lu

  • EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation

    Duo Ding;Bei Yu;Joydeep Ghosh;David Z. Pan

  • A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction

    Tetsuaki Matsunawa;Jhih Rong Gao;Bei Yu;David Z. Pan

  • MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper

    Martin Rapp;Hussam Amrouch;Yibo Lin;Bei Yu

  • GAN-OPC: mask optimization with lithography-guided generative adversarial nets

    Haoyu Yang;Shuhe Li;Yuzhe Ma;Bei Yu

  • MOSAIC: Mask Optimizing Solution With Process Window Aware Inverse Correction

    Jhih-Rong Gao;Xiaoqing Xu;Bei Yu;David Z. Pan

Frequent Co-Authors

David Z. Pan
David Z. Pan The University of Texas at Austin
Evangeline F. Y. Young
Evangeline F. Y. Young Chinese University of Hong Kong
Derong Liu
Derong Liu University of Illinois at Chicago
Charles J. Alpert
Charles J. Alpert Cadence Design Systems
Jiaya Jia
Jiaya Jia Hong Kong University of Science and Technology
Ozgur Sinanoglu
Ozgur Sinanoglu New York University Abu Dhabi
Ulf Schlichtmann
Ulf Schlichtmann Technical University of Munich
Lars W. Liebmann
Lars W. Liebmann Intel (United States)
Ross Baldick
Ross Baldick The University of Texas at Austin
Yier Jin
Yier Jin University of Florida

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Best Scientists Citing Bei Yu

Trending Scientists

Recently Published Articles