Wei Fang

AI Research Scientist at MiroMind

prof_pic.jpg

wei.fang at miromind.ai

fangwei123456g at gmail.com

I am currently an AI research scientist at MiroMind.

I received my Ph.D. degree from the School of Computer Science, Peking University, supervised by Prof. Yonghong Tian. During my Ph.D. career, I closely cooperated with Prof. Zhaofei Yu of Peking University, Researcher Timothée Masquelier of CNRS, and Prof. Guoqi Li of the Institute of Automation, Chinese Academy of Sciences.

My recent research interests include learning algorithms and network structures for Spiking Neural Networks.

Please contact me if you are interested in my research.

中文主页 · English CV · 中文简历

latest posts

May 09, 2026 Site refresh

selected publications

  1. Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
    Wei Fang, Zhaofei Yu, Yanqi Chen, Timothée Masquelier, Tiejun Huang, and Yonghong Tian
    In IEEE/CVF International Conference on Computer Vision, 2021
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  2. Deep Residual Learning in Spiking Neural Networks
    Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian
    In Advances in Neural Information Processing Systems, 2021
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  3. SpikingJelly: An Open-source Machine Learning Infrastructure Platform for Spike-based Intelligence
    Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothée Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, and Yonghong Tian
    Science Advances, 2023
    Recommended by Nature Computational Science in the Research Highlight article: A Full-stack Platform for Spiking Deep Learning.
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  4. Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
    Wei Fang, Zhaofei Yu, Zhaokun Zhou, Ding Chen, Yanqi Chen, Zhengyu Ma, Timothée Masquelier, and Yonghong Tian
    In Advances in Neural Information Processing Systems, 2023
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  5. Review of Surrogate Gradient Methods in Spiking Deep Learning
    Wei Fang, Yaoyu Zhu, Zihan Huang, Man Yao, Zhaofei Yu, and Yonghong Tian
    Chinese Journal of Computers, 2025
    In Chinese.
    PDF
  6. Differential Coding for Training-Free ANN-to-SNN Conversion
    Zihan Huang, Wei Fang, Tong Bu, Peng Xue, Zecheng Hao, Wenxuan Liu, Yuanhong Tang, Zhaofei Yu, and Tiejun Huang
    In International Conference on Machine Learning, 2025
    Co-corresponding
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  7. Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
    Peng Xue, Wei Fang, Zhengyu Ma, Zihan Huang, Zhaokun Zhou, Yonghong Tian, Timothée Masquelier, and Huihui Zhou
    In Advances in Neural Information Processing Systems, 2025
    Co-corresponding
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  8. Parallel Training Time-to-First-Spike Spiking Neural Networks
    Kaiwei Che, Wei Fang, Peng Xue, Yifan Huang, Zhengyu Ma, and Yonghong Tian
    In AAAI Conference on Artificial Intelligence, 2026
    Co-corresponding
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  9. Towards Lossless Memory-efficient Training of Spiking Neural Networks via Gradient Checkpointing and Spike Compression
    Yifan Huang, Wei Fang, Zecheng Hao, Zhengyu Ma, and Yonghong Tian
    In International Conference on Learning Representations, 2026
    Co-corresponding
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  10. Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space
    Wei Fang and Priyadarshini Panda
    In International Conference on Machine Learning, 2026
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