Yujie Wu

Yujie Wu

Assistant Professor

Short Bio

Dr. Yujie Wu is currently an Assistant Professor (Presidential Young Scholar, Ph.D. Supervisor) in the Department of Computing at The Hong Kong Polytechnic University (PolyU). He received his Ph.D. from Tsinghua University in 2021 under the supervision of Prof. Luping Shi and Prof. Jun Zhu, and subsequently conducted postdoctoral research in the group of Prof. Wolfgang Maass at Graz University of Technology, Austria (Oct 2021–Feb 2024). His research interests focus on computational neuroscience and brain-inspired intelligence, with the ultimate goal of understanding and leveraging the brain’s computational principles to develop efficient, intelligent, and sustainable AI systems. His work has been published in leading journals and top conferences, including Nature, Science Robotics, Nature Computational Science, Nature Communications, ICML, and AAAI. These publications have received multiple distinctions, such as cover papers, ESI Top 1% Highly Cited Papers, Featured Articles, Annual Recommended Papers, and Best Paper Awards. Dr. Wu currently serves as an Associate Editor for Neurocomputing. He has been recognized as a Rising Star in Brain–Computer Intelligence in China (2025, inaugural selection) and is featured among the World’s Top 2% Most-Cited Scientists by Stanford University (2024, 2025).

Opening

I am looking for highly self-motivated PhD students, PostDocs, and Research Assistants. If you are interested, please send me your CV. Thanks! (All CVs are carefully evaluated. Only matched candidates will be responded within TWO weeks.)

News

  • [10/2025] Nominated as a Rising Star in Brain–Computer Intelligence in China by the World Association of Young Scientists (1 of 6 recipients nationwide)
  • [09/2025] Featured in the World’s Top 2% Most-Cited Scientists 2025 list by Stanford University (2024, 2025)
  • [09/2025] One paper published in Nature Communications
  • [08/2025] Recognized as a Top Reviewer for NeurIPS 2025
  • [07/2025] Awarded a General Research Fund (GRF) by the Research Grants Council of Hong Kong
  • [05/2025] Invited as an Associate Editor for Neurocomputing （IF=8.18）
  • [05/2025] Awarded the Presidential Young Scholar, supported by a HK$4M grant
  • [04/2025] One paper selected as a Top 50 Featured Paper by the editors of Nature Communications
  • [03/2025] Received a Gifted Research Fund from Huawei
  • [02/2025] Invited as a Guest Editor for IEEE Transactions on Cognitive and Developmental Systems
  • [01/2025] One paper published in Nature Communications
  • [01/2025] Invited as a Guest Editor for Neuromorphic Computing and Engineering

Research Interests

I aim to uncover the computational principles of the brain and bridge the gap between neuroscience and AI to develop Brain-inspired General Intelligence (BGI). My recent research interests include:
  • Brain-inspired intelligence and cognitive LLM models
  • Computational neuroscience models (e.g., memory models and synaptic plasticity)
  • Spiking neural networks
  • Bioinspired robotic applications

Selected Ten Publications (updated as of December 2024)

  1. Y. Wu, W. Maass. "A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning." Nature Communications, 2025. (Featured paper)
  2. Y. Wu, L. Deng, et al. "Adaptive spatiotemporal neural networks through complementary hybridization." Nature Communications, 2024, 15(1): 7355. (Featured paper)
  3. F. Yu# Y. Wu# (#Equal Contribution), M. Song#, et al. "Brain-inspired multimodal hybrid neural network for robot place recognition", Science Robotics, May 2023. (Cover paper)
  4. Y. Wu, R. Zhao, J. Zhu, et al. "Brain-inspired global-local learning incorporated with neuromorphic computing." Nature Communications, 2022, 13(1): 1–14.
  5. R. Zhao#, Y. Zhe#, H. Zheng#, Y. Wu#, et al. "A framework for the general design and computation of hybrid neural networks." Nature Communications, 2022.
  6. Y. Wu, L. Deng, G. Li, et al. "Direct training for spiking neural networks: faster, larger, better." In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2019, 33: 1311–1318. (Spotlight)
  7. Y. Wu, L. Deng, G. Li, et al. "Spatio-temporal backpropagation for training high-performance spiking neural networks." Frontiers in Neuroscience, 2018, 12: 331. (ESI Top 1% Highly Cited Paper)
  8. Z. Zhang#, T. Li#, Y. Wu#, et al. "Truly concomitant and independently expressed short- and long-term plasticity in a Bi₂O₂Se-based three-terminal memristor." Advanced Materials, 2019, 31(3): 1805769. (Impact Factor: 30.2)
  9. J. Pei, ..., Y. Wu, et al. "Towards artificial general intelligence with hybrid Tianjic chip architecture." Nature, 2019, 572(7767): 106–111. (Cover paper)
  10. L. He, Y. Xu, W. He, Y. Lin, Y. Tian, Y. Wu, et al. "Network model with internal complexity bridges artificial intelligence and neuroscience." Nature Computational Science, 2024, 4: 8. (Cover runner-up)

Service Activities

  • Associate Editor for Neurocomputing
  • Guest Editor for Neuromorphic Computing and Engineering
  • Guest Editor for IEEE Transactions on Cognitive and Developmental Systems
  • Associate Editor for PRSC 2024, CIS-RAM 2024
  • Review Editor for Frontiers in Neuroscience
  • Chair member for IC-MIND 2025
  • Program Committee Member for AAAI 2024, 2025
  • Technical Program Committee Member for IEEE GlobCon series
  • Reviewer for Nature series journals and multiple top AI conferences

Selected Talks

  • December 2024, Hangzhou, China. Plasticity Models for Hippocampal One-shot Online Learning, China Brain-Machine Intelligence Conference
  • November 2024, Yunnan, China. Brain-Inspired Hybrid Intelligence, International Youth Forum on Brain and Intelligence
  • October 2024, Xiamen, China. Brain-Inspired Hybrid Intelligence, Nanqiang Forum, Xiamen University
  • July 2024, Salzburg, Austria. One-Shot Learning and Robust Recall with BTSP, Graz University of Technology
  • January 2024, Hong Kong, China. Brain-Inspired Hybrid Global-Local Learning Methods, The Hong Kong Polytechnic University
  • May 2023, Beijing, China. Exploring Brain-Inspired Local and Local-Global Learning with Neuromorphic Computing, Institute of Automation, Chinese Academy of Sciences