World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
7761
World Ranking
8357
National Ranking
1088

Leibo Liu 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 Leibo Liu 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: 519 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Leibo Liu 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 Leibo Liu 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

Leibo Liu is affiliated with Tsinghua University in China and has a research portfolio encompassing computer science and engineering, with a specific focus on electrical and electronic engineering and artificial intelligence. Their work extends into hardware and architecture, computer vision and pattern recognition, and computer networks and communications.

Their recent publications include the following papers:

  • Approximate Arithmetic Circuits: A Survey, Characterization, and Recent Applications (2020), published in Proceedings of the IEEE
  • Highly Efficient Architecture of NewHope-NIST on FPGA using Low-Complexity NTT/INTT (2020), published in IACR Transactions on Cryptographic Hardware and Embedded Systems
  • A 28nm 29.2TFLOPS/W BF16 and 36.5TOPS/W INT8 Reconfigurable Digital CIM Processor with Unified FP/INT Pipeline and Bitwise In-Memory Booth Multiplication for Cloud Deep Learning Acceleration (2022), published in 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • LWRpro: An Energy-Efficient Configurable Crypto-Processor for Module-LWR (2021), published in IEEE Transactions on Circuits and Systems I Regular Papers
  • A Compact and High-Performance Hardware Architecture for CRYSTALS-Dilithium (2021), published in IACR Transactions on Cryptographic Hardware and Embedded Systems

They frequently collaborate with several co-authors, including:

  • Shaojun Wei (70 joint publications)
  • Shouyi Yin (55 joint publications)
  • Jianfeng Zhu (18 joint publications)
  • Bohan Yang (15 joint publications)
  • Wenping Zhu (14 joint publications)

Their research is frequently published in venues such as:

  • IEEE Journal of Solid-State Circuits (19 publications)
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (13 publications)
  • IEEE Transactions on Circuits and Systems I Regular Papers (12 publications)
  • IACR Transactions on Cryptographic Hardware and Embedded Systems (11 publications)
  • IEEE Transactions on Parallel and Distributed Systems (7 publications)

Leibo Liu's main research fields are computer science and engineering, while their subfields of study emphasize electrical and electronic engineering, artificial intelligence, hardware and architecture, computer vision and pattern recognition, and computer networks and communications.

The main topics explored in their research include:

  • Advanced Memory and Neural Computing
  • Ferroelectric and Negative Capacitance Devices
  • Parallel Computing and Optimization Techniques
  • Advanced Neural Network Applications
  • Coding Theory and Cryptography
  • Cryptographic Implementations and Security
  • Embedded Systems Design Techniques

Best Publications

  • Deep Convolutional Neural Network Architecture With Reconfigurable Computation Patterns

    Fengbin Tu;Shouyi Yin;Peng Ouyang;Shibin Tang

  • Approximate Arithmetic Circuits: A Survey, Characterization, and Recent Applications

    Honglan Jiang;Francisco Javier Hernandez Santiago;Hai Mo;Leibo Liu

  • A Review, Classification, and Comparative Evaluation of Approximate Arithmetic Circuits

    Honglan Jiang;Cong Liu;Leibo Liu;Fabrizio Lombardi

  • FP-BNN

    Shuang Liang;Shouyi Yin;Leibo Liu;Wayne Luk

  • Analog circuit optimization system based on hybrid evolutionary algorithms

    Bo Liu;Yan Wang;Zhiping Yu;Leibo Liu

  • A High Energy Efficient Reconfigurable Hybrid Neural Network Processor for Deep Learning Applications

    Shouyi Yin;Peng Ouyang;Shibin Tang;Fengbin Tu

  • A Survey of Coarse-Grained Reconfigurable Architecture and Design: Taxonomy, Challenges, and Applications

    Leibo Liu;Jianfeng Zhu;Zhaoshi Li;Yanan Lu

  • A 28nm 29.2TFLOPS/W BF16 and 36.5TOPS/W INT8 Reconfigurable Digital CIM Processor with Unified FP/INT Pipeline and Bitwise In-Memory Booth Multiplication for Cloud Deep Learning Acceleration

    Unknown

  • Highly Efficient Architecture of NewHope-NIST on FPGA using Low-Complexity NTT/INTT

    Neng Zhang;Bohan Yang;Chen Chen;Shouyi Yin

  • A 1.06-to-5.09 TOPS/W reconfigurable hybrid-neural-network processor for deep learning applications

    Shouyi Yin;Peng Ouyang;Shibin Tang;Fengbin Tu

  • Multibank memory optimization for parallel data access in multiple data arrays

    Shouyi Yin;Zhicong Xie;Chenyue Meng;Leibo Liu

  • A 5.1pJ/Neuron 127.3us/Inference RNN-based Speech Recognition Processor using 16 Computing-in-Memory SRAM Macros in 65nm CMOS

    Ruiqi Guo;Yonggang Liu;Shixuan Zheng;Ssu-Yen Wu

  • FACT: FFN-Attention Co-optimized Transformer Architecture with Eager Correlation Prediction

    Unknown

  • Polyhedral model based mapping optimization of loop nests for CGRAs

    Dajiang Liu;Shouyi Yin;Leibo Liu;Shaojun Wei

  • A 28nm 15.59µJ/Token Full-Digital Bitline-Transpose CIM-Based Sparse Transformer Accelerator with Pipeline/Parallel Reconfigurable Modes

    Unknown

  • RANA: towards efficient neural acceleration with refresh-optimized embedded DRAM

    Fengbin Tu;Weiwei Wu;Shouyi Yin;Leibo Liu

  • A Compact and High-Performance Hardware Architecture for CRYSTALS-Dilithium

    Cankun Zhao;Neng Zhang;Hanning Wang;Bohan Yang

  • A 141 UW, 2.46 PJ/Neuron Binarized Convolutional Neural Network Based Self-Learning Speech Recognition Processor in 28NM CMOS

    Shouyi Yin;Peng Ouyang;Shixuan Zheng;Dandan Song

  • An Implementation of Fast-Locking and Wide-Range 11-bit Reversible SAR DLL

    Lei Wang;Leibo Liu;Hongyi Chen

  • A VLSI architecture of JPEG2000 encoder

    Leibo Liu;Ning Chen;Hongying Meng;Li Zhang

  • ReDCIM: Reconfigurable Digital Computing- In -Memory Processor With Unified FP/INT Pipeline for Cloud AI Acceleration

    Unknown

  • LWRpro: An Energy-Efficient Configurable Crypto-Processor for Module-LWR

    Yihong Zhu;Min Zhu;Bohan Yang;Wenping Zhu

  • 9.2A 28nm 12.1TOPS/W Dual-Mode CNN Processor Using Effective-Weight-Based Convolution and Error-Compensation-Based Prediction

    Huiyu Mo;Wenping Zhu;Wenjing Hu;Guangbin Wang

  • An Energy-Efficient Reconfigurable Processor for Binary-and Ternary-Weight Neural Networks With Flexible Data Bit Width

    Shouyi Yin;Peng Ouyang;Jianxun Yang;Tianyi Lu

  • A High-Performance and Energy-Efficient FIR Adaptive Filter Using Approximate Distributed Arithmetic Circuits

    Honglan Jiang;Leibo Liu;Pieter P. Jonker;Duncan G. Elliott

Frequent Co-Authors

Shaojun Wei
Shaojun Wei Tsinghua University
Shouyi Yin
Shouyi Yin Tsinghua University
Jie Han
Jie Han University of Alberta
Fabrizio Lombardi
Fabrizio Lombardi Northeastern University
Zhihua Wang
Zhihua Wang Tsinghua University
Hongying Meng
Hongying Meng Brunel University London
Sheng Zhou
Sheng Zhou Tsinghua University
Meng-Fan Chang
Meng-Fan Chang National Tsing Hua University
Yuan Xie
Yuan Xie Hong Kong University of Science and Technology
Yiyu Shi
Yiyu Shi University of Notre Dame

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