World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
5846
World Ranking
8516
National Ranking
1111

Yun Liang 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 Yun Liang 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: 131 publications — 19th percentile

19% of scientists in this discipline score the same or lower.

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

Yun Liang 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 Yun Liang 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Yun Liang is affiliated with Peking University in China and has contributed extensively to research primarily within computer science and engineering. Their work spans multiple subfields including electrical and electronic engineering, computer vision and pattern recognition, hardware and architecture, artificial intelligence, and biomedical engineering.

Their research topics cover a range of applied and theoretical areas such as parallel computing and optimization techniques, aerosol filtration and electrostatic precipitation, advanced neural network applications, embedded systems design techniques, advanced sensor and energy harvesting materials, advanced image and video retrieval techniques, and advancements in battery materials.

Some of Yun Liang's recent publications include:

  • "Tillandsia-Inspired Hygroscopic Photothermal Organogels for Efficient Atmospheric Water Harvesting" (2020), published in Angewandte Chemie International Edition
  • "Structural insights into composition design of Li-rich layered cathode materials for high-energy rechargeable battery" (2021), published in Materials Today
  • "Graphene wrapped silicon suboxides anodes with suppressed Li-uptake behavior enabled superior cycling stability" (2020), published in Energy Storage Materials
  • "Biomimetic underwater self-perceptive actuating soft system based on highly compliant, morphable and conductive sandwiched thin films" (2020), published in Nano Energy
  • "An Efficient Hardware Design for Accelerating Sparse CNNs With NAS-Based Models" (2021), published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Yun Liang's frequent coauthors include Min Tang, Liqiang Lu, Guilong Xu, Tianle You, and Peng Xiao. This collaborative network indicates active engagement in multidisciplinary research projects.

Regarding publication venues, Yun Liang has contributed significantly to journals and platforms such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • SSRN Electronic Journal
  • Separation and Purification Technology
  • Advanced Functional Materials

Yun Liang's research output demonstrates a focus on integrating advanced computing techniques with material science and engineering applications. Their work intersects development in hardware design, neural networks, and material innovations, reflecting diverse expertise and interests within both theoretical and applied domains.

Best Publications

  • Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs

    Xuechao Wei;Cody Hao Yu;Peng Zhang;Youxiang Chen

  • Chronos: A timing analyzer for embedded software

    Xianfeng Li;Yun Liang;Tulika Mitra;Abhik Roychoudhury

  • Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs

    Liqiang Lu;Yun Liang;Qingcheng Xiao;Shengen Yan

  • C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

    Shuo Wang;Zhe Li;Caiwen Ding;Bo Yuan

  • Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs

    Qingcheng Xiao;Yun Liang;Liqiang Lu;Shengen Yan

  • Timing analysis of concurrent programs running on shared cache multi-cores

    Yun Liang;Huping Ding;Tulika Mitra;Abhik Roychoudhury

  • Timing Analysis of Concurrent Programs Running on Shared Cache Multi-Cores

    Yan Li;Vivy Suhendra;Yun Liang;Tulika Mitra

  • Sanger: A Co-Design Framework for Enabling Sparse Attention using Reconfigurable Architecture

    Liqiang Lu;Yicheng Jin;Hangrui Bi;Zizhang Luo

  • Coordinated static and dynamic cache bypassing for GPUs

    Xiaolong Xie;Yun Liang;Yu Wang;Guangyu Sun

  • FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System

    Size Zheng;Yun Liang;Shuo Wang;Renze Chen

  • An Efficient Hardware Accelerator for Sparse Convolutional Neural Networks on FPGAs

    Liqiang Lu;Jiaming Xie;Ruirui Huang;Jiansong Zhang

  • Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs

    Yun Liang;Liqiang Lu;Qingcheng Xiao;Shengen Yan

  • Lin-analyzer: a high-level performance analysis tool for FPGA-based accelerators

    Guanwen Zhong;Alok Prakash;Yun Liang;Tulika Mitra

  • An efficient compiler framework for cache bypassing on GPUs

    Xiaolong Xie;Yun Liang;Guangyu Sun;Deming Chen

  • Efficient GPU Spatial-Temporal Multitasking

    Yun Liang;Huynh Phung Huynh;Kyle Rupnow;Rick Siow Mong Goh

  • High-level synthesis: productivity, performance, and software constraints

    Yun Liang;Kyle Rupnow;Yinan Li;Dongbo Min

  • REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

    Caiwen Ding;Shuo Wang;Ning Liu;Kaidi Xu

  • Improving high level synthesis optimization opportunity through polyhedral transformations

    Wei Zuo;Yun Liang;Peng Li;Kyle Rupnow

  • Hi-fi playback: tolerating position errors in shift operations of racetrack memory

    Chao Zhang;Guangyu Sun;Xian Zhang;Weiqi Zhang

  • Enabling coordinated register allocation and thread-level parallelism optimization for GPUs

    Xiaolong Xie;Yun Liang;Xiuhong Li;Yudong Wu

  • SpWA: an efficient sparse winograd convolutional neural networks accelerator on FPGAs

    Liqiang Lu;Yun Liang

  • COMBA: a comprehensive model-based analysis framework for high level synthesis of real applications

    Jieru Zhao;Liang Feng;Sharad Sinha;Wei Zhang

Frequent Co-Authors

Tulika Mitra
Tulika Mitra National University of Singapore
Deming Chen
Deming Chen University of Illinois at Urbana-Champaign
Jason Cong
Jason Cong University of California, Los Angeles
Guangyu Sun
Guangyu Sun Peking University
Wei Tan
Wei Tan Citadel LLC
Abhik Roychoudhury
Abhik Roychoudhury National University of Singapore
Bingsheng He
Bingsheng He National University of Singapore
Qinru Qiu
Qinru Qiu Syracuse University
Yanzhi Wang
Yanzhi Wang Northeastern University
Wen-mei W. Hwu
Wen-mei W. Hwu University of Illinois at Urbana-Champaign

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