Wei Fang (方维)

Apodex 人工智能研究科学家

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fangwei123456g at gmail.com

我目前是 Apodex 的人工智能研究科学家。

我在北京大学计算机学院获得博士学位，指导老师为田永鸿教授。博士期间，我也与北京大学的余肇飞教授、法国国家科学研究中心的 Timothée Masquelier 研究员、中国科学院自动化研究所的李国齐教授长期合作。

我的主要研究方向包括大模型 infra、神经形态计算、类脑计算。

欢迎对我研究感兴趣的团队联系我。

English homepage · 中文简历 · English CV

教育和工作经历

部分奖励荣誉

  • 2023 年度“石青云院士优秀论文奖”
  • 2024 年度北京大学优秀毕业生
  • 2024 年度北京市普通高等学校优秀毕业生
  • 2024 年度北京大学优秀博士学位论文
  • 2025 年度 CCF 博士学位论文激励计划（CCF 优博）

项目

更多内容见论文列表、项目页和博客。

最新博客文章

May 11, 2026 一作论文的中文版翻译上线
May 09, 2026 Site refresh

精选论文

  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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