Shenghui Wu

Shenghui Wu

Research Assistant Professor @ HKUST

Shenghui WU (伍圣晖) received his B.Eng. degree from Huazhong University of Science and Technology (HUST) in 2019. He earned his MPhil and Ph.D. degree from the Hong Kong University of Science and Technology (HKUST) in 2021 and 2024, respectively, under the supervision of Prof. Yiwen Wang in the Computational Cognitive Engineering Lab lab. He is now a Research Assistant Professor in HKUST.

His research interests include brain-machine interfaces, neural engineering, and reinforcement learning. He has publications at top journals and conferences, including Nature Computational Science, IEEE TNSRE, Journal of Neural Engineering, etc. He is a reviewer for several conferences and journals. He served as a session chair for IJCNN 2025 and a member of the organization committee for the 4th International Workshop on Neural Engineering and Rehabilitation.

🔥 News

  • 2026.01.05-01.09:  🎉🎉 I am nominated to participate in the Global Young Scientists Summit (GYSS) 2026. See you in Singapore!
  • 2025.12.12:  🎉🎉 Our study was featured as a Spotlight Poster at the 2nd Chinese Conference on Brain-Machine Intelligence!
  • 2025.08.05:   Three papers are accepted by EMBC NER 2025

📝 Publications

2026

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[J5] A generative spike prediction model using behavioral reinforcement for re-establishing neural functional connectivity

Shenghui Wu, Zhiwei Song, Xiang Zhang, Yifan Huang, Shuhang Chen, Xiang Shen, Jieyuan Tan, Mingdong Li, Ziyi Wang, Yujun Chen, Kai Liu, Dario Farina, Jose C. Principe, Yiwen Wang. In Nature Computational Science.

Paper | Code

  • The study presents a generative spike-based framework to re-establish functional connectivity across pathway-damaged brain regions, enabling biomimetic neural prostheses and closed-loop brain stimulation.

2025

  • [C7] Zhiwei Song, Shenghui Wu, Taiyan Zhou, Yiwen Wang. Extracting Preserved Neural Latent Dynamics Across Tasks using Convolutional Transformer-based Variational Autoendecoder. In Proceedings of 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 🔗[Paper]
  • [C8] Shenghui Wu, Xiang Zhang, Yiwen Wang. Behavior-Reinforced Latent Alignment for Generating Functional Neural Spike Patterns. In Proceedings of 2025 International Joint Conference on Neural Networks (IJCNN). 🔗[Paper]

2024

  • [C4] Shenghui Wu, Xiang Zhang, Yifan Huang, Yiwen Wang. Aligning Transregional Neural Dynamics with Transformer-based Variational Autoencoders. In Proceedings of 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 🔗[Paper]
  • [C5] Shicheng Qiu, Hongwei Mao, Shenghui Wu, Yiwen Wang. Investigating Internal Dynamics in Monkey’s Primary Motor Cortex during Reaching. In Proceedings of 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 🔗[Paper]
  • [J3] Shenghui Wu, Xiang Zhang, Yiwen Wang. Neural Manifold Constraint for Spike Prediction Models under Behavioral Reinforcement. In IEEE Transactions on Neural Systems and Rehabilitation Engineering 🔗[Paper]
  • [C6] Mingyi Wang, Jieyuan Tan, Yifan Huang, Shenghui Wu, Zhiwei Song, Yiwen Wang. Extracted Audio-Induced Reward Expectation Information from Local Field Potential in the Medial Prefrontal Cortex. In Proceedings of 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 🔗[Paper]
  • [J4] Jieyuan Tan, Xiang Zhang, Shenghui Wu, Zhiwei Song, Yiwen Wang. Hidden Brain State-based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-machine Interfaces. In IEEE Transactions on Neural Systems and Rehabilitation Engineering. 🔗[Paper]

2023

  • [C2] Shenghui Wu (Corresponding) and Yiwen Wang. Applying Neural Manifold Constraint on Point Process Model for Neural Spike Prediction. In Proceedings of 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 🔗[Paper]
  • [C3] Jieyuan Tan, Xiang Zhang, Shenghui Wu, Yiwen Wang. State-space Model Based Inverse Reinforcement Learning for Reward Function Estimation in Brain-machine Interfaces. In Proceedings of 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 🔗[Paper]
  • [J2] Jieyuan Tan, Xiang Zhang, Shenghui Wu, Zhiwei Song, Shuhang Chen, Yifan Huang, Yiwen Wang. Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain–machine interfaces, In Journal of Neural Engineering. 🔗[Paper]

2022

  • [J1] Shenghui Wu, Cunle Qian, Xiang Shen, Xiang Zhang, Yifan Huang, Shuhang Chen, Yiwen Wang. Spike prediction on primary motor cortex from medial prefrontal cortex during task learning. In Journal of Neural Engineering. 🔗[Paper]

2020

  • [C1] Shenghui Wu, Cunle Qian, Xiang Shen, Xiang Zhang, Yifan Huang, Shuhang Chen, Yiwen Wang. Investigating Co-Activation between Medial Prefrontal and Primary Motor Cortical Spike Trains during Task Learning. In Proceedings of 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 🔗[Paper]

🎖 Honors and Awards

  • 2024, 2023 HKUST RedBird Academic Excellence Award
  • 2024 NextGen Scholar Award, IEEE EMBC
  • 2023 HKUST ECE Best Teaching Assistant Award 2022/23
  • 2019 HUST Outstanding Graduate
  • 2018 National Encouragement Scholarship
  • 2018 First Prize of Microchip China Scholarship

🎓 Educations

  • 2021.09 - 2024.08, Ph.D, The Hong Kong University of Science and Technology.
  • 2019.09 - 2021.08, MPhil, The Hong Kong University of Science and Technology.
  • 2015.09 - 2019.06, B.Eng, Huazhong University of Science and Technology.

🖐️ Services

  • Reviewer for J. Neural Eng., EMBC 2025, IJCNN 2025, EMBC NER 2025.
  • Session Chair of IJCNN 2025.
  • Ambassador for 2023 IEEE EMBS Student Mentoring Program (Online).
  • Organization Committee member for the 4th International Workshop on Neural Engineering and Rehabilitation (Chengdu, China).

💬 Invited Talks

  • 2024.05, Reinforcement Learning-based Spike Prediction for Transregional Neural Prostheses. In ECE Future Leaders PG Seminar.

📖 Teaching

  • 2022 Fall, 2020 Fall, Teaching Assistant, Signals and Systems (ELEC 2100).
  • 2022 Spring, Teaching Assistant, Machine Learning on Images (ELEC 4130).
  • 2020 Spring, Teaching Assistant, Statistical Signal Analysis and Applications in Neural Engineering (ELEC 4830 & BIEN 4310).

💻 Working Experience