Wentai Wu received his Bachelor and Master degrees from South China University of Technology in 2015 and 2018, respectively. Sponsored by CSC, he received the Ph.D. degree in Computer Science in 2022 from the University of Warwick, United Kingdom. Between 2022 and 2023 he worked as a researcher with Pengcheng Laboratory. Since 2024 he has been with the Department of Computer Science, College of Information Science and Technology, Jinan University, as an associate professor. His research interests mainly include distributed intelligent systems, collaborative data mining and sustainable computing. He has published over 30 research papers and contributed as chapter author to two books on relevant subjects. He also serves as reviewer for high-impact journals including IEEE TPDS, TC, TBD and TSUSC, and as organizing committee member or PC member for conferences such as Bench, ISPA, ICML and NeurIPS. He won the IEEE Comp. Soc. best paper award runner-up (year 2021) and received the 2020 Guangdong S&T Progress Award. He was listed among the Stanford-released top 2% scientists in Distributed Computing subfield in 2023 and 2024.

2011-2015, South China University of Technology, B.E.

2015-2018, South China University of Technology, M.E.

2018-2022, University of Warwick, Ph.D. in Computer Science

• Lin, W., Wang, S., Wu, W.*, Li, D., & Zomaya, A. (2023) HybridAD: A Hybrid Model-driven Anomaly Detection Approach for Multivariate Time Series. IEEE Transactions on Emerging Topics in Computational Intelligence. Vol.8, no.1, pp.866-878. DOI: 10.1109/TETCI.2023.3290027. [JCR-Q2, IF 5.3]

• Wu, W., He, L.*, Lin, W.*, & Maple, C. (2023) FedProf: Selective Federated Learning based on Distributional Representation Profiling. IEEE Transactions on Parallel and Distributed Systems (TPDS). Vol. 34, no. 6, pp. 1942-1953. DOI: 10.1109/TPDS.2023.3265588. [CCF-A, JCR-Q1, IF 5.3]

• Lin, W., Xiong, C.*, Wu, W.*, Shi, F., Li, K., & Xu, M. (2022). Performance Interference of Virtual Machines: A Survey. ACM Computing Surveys. Vol. 55, no. 12, pp. 1-37 [JCR-Q1, IF 16.6]

• Wu, W., He, L.*, Lin, W., & Mao, R. (Jul, 2021) Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems. IEEE Transactions on Parallel and Distributed Systems (TPDS). vol. 32, no.7, pp. 1539-1551. [CCF-A, JCR-Q1, IF 5.3]

• Wu, W., He, L.*, Lin, W., Mao, R., & Jarvis, S. (Jun, 2021). SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead. IEEE Transactions on Computers (TC). vol. 70, no.5, pp. 655-668. [CCF-A, JCR-Q2, IF 3.7, IEEE Computer Society 2021 Best Paper Award Runner-up (from IEEE TC)]

• Wu, W., He, L.*, Lin, W. et al. (Sep, 2022). Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality. IEEE Transactions on Knowledge and Data Engineering (TKDE). Vol. 34, no. 9, pp. 4147-4160. [CCF-A, JCR-Q1, IF 8.9]

• Wu, W., Lin, W.*, He, L., Wu, G., & Hsu, C. (Apr, 2021). A Power Consumption Model for Cloud Servers Based on Elman Neural Network. IEEE Transactions on Cloud Computing (TCC). Vol. 9, no. 4, pp. 1268-1277. [JCR-Q1, IF 6.5]

• Lin, W., Wu, W.*, & He, L.(Mar, 2022). An On-line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers. IEEE Transactions on Services Computing (TSC). Vol. 15, no. 2, pp. 766-777. [CCF-A, JCR-Q1, IF 8.1]