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Dr Weibo Liu
Lecturer in Computer Science


Dr. Weibo Liu received the B.S. degree in electrical engineering from the Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, UK, in 2015, and the Ph.D. degree in artificial intelligence in 2020 from the Department of Computer Science, Brunel University London, Uxbridge, UK.

He is currently Lecturer in the Department of Computer Science, Brunel University London, U.K. His research interests include big data analysis, evolutionary computation, machine learning and deep learning techniques. He serves as an Associate Editor for the Journal of Ambient Intelligence and Humanized Computing and the Journal of Cognitive Computation. He is a very active reviewer for many international journals and conferences.


Program Committees I am / have been on the program committee for the 25th International Conference on Automation and Computing (ICAC) 2017.

Newest selected publications

Guijun, M., Wang, Z., Weibo, L., Jingzhong, F., Yong, Z., Han, D. and et al. (2023) 'Estimating the state of health for Lithium-ion batteries: a particle swarm optimization-assisted deep domain adaptation approach'. IEEE/CAA Journal of Automatica Sinica, 10 (7). pp. 1530 - 1543. ISSN: 2329-9266

Journal article

Zhang, J., Wang, Z., Liu, W., Liu, X. and Zheng, Q. (2023) 'A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors'. International Journal of Machine Learning and Cybernetics, 14 (9). pp. 2903 - 2912. ISSN: 1868-8071 Open Access Link

Journal article

Chen, M., Ma, G., Liu, W., Zeng, N. and Luo, X. (2023) 'An Overview of Data-driven Battery Health Estimation Technology for Battery Management System'. Neurocomputing, 532. pp. 152 - 169. ISSN: 0925-2312

Journal article

Ma, G., Wang, Z., Liu, W., Fang, J., Zhang, Y., Ding, H. and et al. (2023) 'A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries'. Knowledge-Based Systems, 259. pp. 1 - 10. ISSN: 0950-7051 Open Access Link

Journal article

Wang, C., Wang, Z., Liu, W., Shen, Y. and Dong, H. (2023) 'A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples'. IEEE Transactions on Instrumentation and Measurement, 72. pp. 1 - 13. ISSN: 0018-9456 Open Access Link

Journal article
More publications(31)