Zhou Wei
- IMIC Lab
- People , Engineering teacher
- July 3, 2024
Zhou Wei, Ph.D., is a lecturer at the School of Artificial Intelligence of Nanjing University of Information Science & Technology, and a core member of the Jiangsu University Key Laboratory of Intelligent Medical Image Computing and the Smart Medical Research Institute. Obtained a bachelor’s degree in engineering (electronic information engineering) from Sichuan University in June 2019, and a doctorate in engineering (biomedical engineering) from Fudan University in June 2024. His supervisor is Professor Chen Wei. During his PhD, he participated in many scientific research projects such as the National Natural Science Foundation of China’s key projects, the Ministry of Science and Technology’s key research and development plan projects, and the Shanghai Municipal Science and Technology Commission’s major projects.
His main research directions and interests are: deep learning, signal processing, intelligent sensing, sleep monitoring, brain-computer interface, brain activity signal monitoring and analysis, focusing on the intersection of artificial intelligence and biomedicine. He has rich experience in physiological signal data processing and analysis, especially focusing on related research in the field of sleep health. During his Ph.D., he published many SCI papers as the first/co-author, including IEEE Journal of Biomedical and Health Informatics (J-BHI), IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE) and other academic journals.
For more information, please visit my personal homepage: https://faculty.nuist.edu.cn/zhouwei/zh_CN/index.htm
Representative papers:
[1] W. Zhou, N. Shen, L, Zhou, et al., “PSEENet: A pseudo-siamese neural network incorporating electroencephalography and electrooculography characteristics for heterogeneous sleep staging,” in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3403878. (District 2, Chinese Academy of Sciences, IF: 7.7)
[2] W. Zhou, H. Zhu, N, Shen, et al., “A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 238-247, 2023. (District 2, Chinese Academy of Sciences, IF: 4.9)
Translated from the original Chinese source.
