Yujie Wu

Yujie Wu

Research Assistant Professor at The Hong Kong Polytechnic University

Biography

Dr. Yujie Wu is currently a Research Assistant Professor at The Hong Kong Polytechnic University (PolyU). Prior to joining PolyU, he was a postdoctoral researcher collaborating with Prof. Wolfgang Maass, a pioneer in spiking neural networks, at the Institute of Theoretical Computer Science, Graz University of Technology, from October 2021 to January 2024. During this period, he focused on brain-like computation and computational neuroscience. His doctoral research, conducted at Tsinghua University under the supervision of Prof. Luping Shi and Prof. Jun Zhu, centered on brain-inspired computing (2016-2021).

He is dedicated to (1) Exploring the computational principles of the brain, with a specific emphasis on underlying learning mechanisms such as STDP and BTSP; (2) Leveraging these insights to develop brain-inspired general intelligence with exceptional computational and learning efficiency (such as robustness, adaptability, and few-shot learning capabilities)

In the past five years, his work have been recognized in prestigious journals and top AI conferences, including Nature (Cover paper), Science Robotics (Cover paper), Nature Computational Science(Cover runner-up), Nature Communications (Featured articles) and AAAI (Spotlight), with more than 5k google scholar citations. Two of his works have been selected as ESI Top 1% Highly Cited Papers. One of his papers was awarded Best Paper of the Year by the Journal of Control and Decision, and Four of his related works have been successfully converted into patents. His participated Tianjic chip project was recognized as one of the top 10 Sci-tech achievements in China in 2019.

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.

  2. Y. Wu, L. Deng, et al. “Adaptive spatiotemporal neural networks through complementary hybridization.” Nature Communications, 2024, 15(1): 7355. (Featured papers)

  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

  • Guest Editor for NCE
  • Associate Editor for CIS-RAM 2024
  • Review Editor for Frontiers in Neuroscience
  • Technical Chair for PRSC 2024
  • Program Committee Member for AAAI 2025
  • Technical Program Committee Member for the IEEE GlobCon series

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

Hiring

I am seeking motivated Post-Doctoral Fellows, Research Assistants, and Ph.D. students with a strong interest and experience in brain-inspired computing and computational neuroscience. If you are interested in joining my research team at PolyU, please email your CV (including research interests, GPA, publications, etc.) and transcript (required for Research Assistant and Ph.D. applicants).