World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
40
Citations
9824
World Ranking
7171
National Ranking
1318

Overview

Luping Shi is affiliated with Tsinghua University in China and has contributed extensively to the fields of engineering and computer science. Their research spans multiple subfields including electrical and electronic engineering, artificial intelligence, computer vision and pattern recognition, cognitive neuroscience, and cellular and molecular neuroscience.

Their work frequently addresses topics such as advanced memory and neural computing, ferroelectric and negative capacitance devices, neural networks and reservoir computing, neural dynamics and brain function, CCD and CMOS imaging sensors, neuroscience and neural engineering, and robotics and sensor-based localization.

Several notable recent publications illustrate the scope and focus of their research:

  • Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey (2020, Proceedings of the IEEE)
  • A system hierarchy for brain-inspired computing (2020, Nature)
  • Tianjic: A Unified and Scalable Chip Bridging Spike-Based and Continuous Neural Computation (2020, IEEE Journal of Solid-State Circuits)
  • Brain-inspired global-local learning incorporated with neuromorphic computing (2022, Nature Communications)
  • Brain-inspired multimodal hybrid neural network for robot place recognition (2023, Science Robotics)

Luping Shi frequently publishes in venues such as arXiv (Cornell University), Nature Communications, Nature, Science Robotics, and Nature Electronics. These publication venues reflect a focus on interdisciplinary research combining engineering, neuroscience, and computer science.

Collaborations play a significant role in their research output, with frequent co-authors including Rong Zhao, Jing Pei, Yujie Wu, Songchen Ma, and Guoqi Li. These partnerships underscore a collaborative approach toward advancing knowledge in related areas of neural computing and brain-inspired technologies.

Best Publications

  • Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks.

    Yujie Wu;Lei Deng;Lei Deng;Guoqi Li;Jun Zhu

  • Towards artificial general intelligence with hybrid Tianjic chip architecture.

    Jing Pei;Lei Deng;Sen Song;Sen Song;Mingguo Zhao

  • Creation of a needle of longitudinally polarized light in vacuum using binary optics

    Haifeng Wang;Luping Shi;Boris Lukyanchuk;Colin Sheppard

  • Breaking the Speed Limits of Phase-Change Memory

    D. Loke;D. Loke;D. Loke;T. H. Lee;W. J. Wang;L. P. Shi

  • Face classification using electronic synapses

    Peng Yao;Huaqiang Wu;Bin Gao;Sukru Burc Eryilmaz

  • Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey

    Lei Deng;Guoqi Li;Song Han;Luping Shi

  • Direct Training for Spiking Neural Networks: Faster, Larger, Better

    Yujie Wu;Lei Deng;Guoqi Li;Jun Zhu

  • CIFAR10-DVS: An Event-Stream Dataset for Object Classification.

    Hongmin Li;Hanchao Liu;Xiangyang Ji;Guoqi Li

  • Training and Inference with Integers in Deep Neural Networks

    Shuang Wu;Guoqi Li;Feng Chen;Luping Shi

  • Phase change random access memory cell with superlattice-like structure

    T. C. Chong;L. P. Shi;R. Zhao;P. K. Tan

  • Fabrication of nanostructures with laser interference lithography

    Q. Xie;M.H. Hong;M.H. Hong;H.L. Tan;G.X. Chen

  • Laser precision engineering: from microfabrication to nanoprocessing

    Tow C. Chong;Minghui H. Hong;Minghui H. Hong;Luping P. Shi

  • Fast phase transitions induced by picosecond electrical pulses on phase change memory cells

    W. J. Wang;L. P. Shi;R. Zhao;K. G. Lim

  • A system hierarchy for brain-inspired computing

    Youhui Zhang;Peng Qu;Yu Ji;Weihao Zhang

  • $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks

    Shuang Wu;Guoqi Li;Lei Deng;Liu Liu

  • Tianjic: A Unified and Scalable Chip Bridging Spike-Based and Continuous Neural Computation

    Lei Deng;Guanrui Wang;Guoqi Li;Shuangchen Li

  • Direct Training for Spiking Neural Networks: Faster, Larger, Better

    Yujie Wu;Lei Deng;Guoqi Li;Jun Zhu

  • Memristor devices for neural networks

    Hongsik Jeong;Luping Shi

  • Truly Concomitant and Independently Expressed Short‐ and Long‐Term Plasticity in a Bi 2 O 2 Se‐Based Three‐Terminal Memristor

    Ziyang Zhang;Tianran Li;Yujie Wu;Yinjun Jia

  • Highly Compact Artificial Memristive Neuron with Low Energy Consumption.

    Yishu Zhang;Wei He;Yujie Wu;Kejie Huang

  • Ultrafast-laser-induced parallel phase-change nanolithography

    Y Lin;MH Hong;TC Chong;CS Lim

  • Role of Ge Switch in Phase Transition: Approach using Atomically Controlled GeTe/Sb2Te3 Superlattice

    Juniji Tominaga;Paul Fons;Alexander Kolobov;Takayuki Shima

  • Brain-inspired global-local learning incorporated with neuromorphic computing

    Yujie Wu;Rong Zhao;Jun Zhu;Feng Chen

  • Training and Inference with Integers in Deep Neural Networks

    Shuang Wu;Guoqi Li;Feng Chen;Luping Shi

  • Enabling an integrated rate-temporal learning scheme on memristor.

    Wei He;Kejie Huang;Ning Ning;Kiruthika Ramanathan

  • Direct femtosecond laser nanopatterning of glass substrate by particle-assisted near-field enhancement

    Y. Zhou;M. H. Hong;J. Y H Fuh;L. Lu

Frequent Co-Authors

Tow Chong Chong
Tow Chong Chong Singapore University of Technology and Design
Guoqi Li
Guoqi Li Shanghai Jiao Tong University
Yee-Chia Yeo
Yee-Chia Yeo National University of Singapore
Minghui Hong
Minghui Hong National University of Singapore
Changyun Wen
Changyun Wen Nanyang Technological University
James A. Bain
James A. Bain Carnegie Mellon University
Jisheng Pan
Jisheng Pan Agency for Science, Technology and Research
Zengbo Wang
Zengbo Wang Bangor University
Gaoxi Xiao
Gaoxi Xiao Nanyang Technological University
Jun Zhu
Jun Zhu Tsinghua University

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