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 an assistant researcher with Peng Cheng Laboratory. Since 2024 he has been with the College of Information Science and Technology, Jinan University, as an Associate Professor. His research interests mainly include distributed systems, edge intelligence, sustainable computing and collaborative machine learning. He has published over 20 research papers and serves as reviewer for high-impact journals and conferences such as IEEE TPDS, TMC, TBD, ICML and NeurIPS, and contributed as chapter author to two books in relevant subjects. 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 top 2% scientists in Distributed Computing subfield in 2023 (single year, per composite citation indicator, by Stanford).

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]