Ning Lu PhD, P.Eng

Dr. Ning Lu is an Associate Professor in the Department of Electrical and Computer Engineering at Queen’s University and holds a Tier 2 Canada Research Chair in Future Communication Networks. He received his BEng (2007) and MEng (2010) degrees from Tongji University, Shanghai, China, and his PhD (2015) from the University of Waterloo, all in Electrical Engineering. Before joining Queen’s, Dr. Lu was an Assistant Professor in the Department of Computing Science at Thompson Rivers University. He was also a Postdoctoral Fellow at the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign (2015–2016), and held a research internship at the National Institute of Informatics, Tokyo, in 2009.

His research focuses on the design, optimization, and deployment of next-generation wireless networks, edge computing systems, and distributed machine learning algorithms. He has published extensively in top-tier venues such as IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, ACM MobiHoc, and IEEE INFOCOM. He currently serves as an Editor for the IEEE Transactions on Wireless Communications and chairs the Special Interest Group on AI-Empowered Internet of Vehicles (IoV) under the IEEE Cognitive Networks Technical Committee. He is also a regular TPC member for major conferences such as IEEE INFOCOM, ACM MobiHoc, IEEE GLOBECOM, and IEEE ICC.

Connected Intelligence Research Lab

Research Interests: Scheduling, computing, learning in communication networks, with applications to Internet of Things, autonomous vehicles, data centers, etc. 

For more information about Dr. Lu's research, visit the Connected Intelligence Research Lab page.

Also, visit his Google Scholar Profile

• ELEC 373 Computer Networks, Winter 2020, 2022, 2023, 2024
• ELEC 829 Optimization for Machine Learning, Winter 2024
• ELEC 860 Communication Network Analysis, Winter 2021, 2022
• ELEC 326 Probability and Random Processes, Fall 2021

Recent Publications 

• J. Steiger, Bin Li, and N. Lu, "Backlogged Bandits: Cost-Effective Learning for Utility Maximization in Queueing Networks," In Proc. IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada, May 2024. [Acceptance rate: 19.6%]
• K.M. Mahfujul, K. Qu, Q. Ye, and N. Lu. "Augmenting Backpressure Scheduling and Routing for Wireless Computing Networks," In Proc. IEEE International Conference on Communications (ICC), Rome, Italy, May 2023.
• J. Steiger, B. Li, B. Ji, and N. Lu, "Constrained Bandit Learning with Switching Costs for Wireless Networks," In Proc. IEEE International Conference on Computer Communications (INFOCOM), New Jersey, USA, May 2023. [Acceptance rate: 19.2%]
• M. Beitollahi and N. Lu, "Federated Learning over Wireless Networks: Challenges and Solutions," IEEE Internet of Things Journal (IoTJ), Vol. 10, No. 16, pp. 14749-14763, 2023.
• M. Beitollahi and N. Lu, "FLAC: Federated Learning with Autoencoder Compression and Convergence Guarantee,” In Proc. IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, Dec. 2022.
• J. Steiger, B. Li, and N. Lu, "Learning from Delayed Semi-Bandit Feedback under Strong Fairness Guarantees," In Proc. IEEE International Conference on Computer Communications (INFOCOM), May 2022. [Acceptance rate: 19.9%] 
• X. Kong, N. Lu, and B. Li, "Optimal Scheduling for Unmanned Aerial Vehicle Networks with Flow-Level Dynamics," IEEE Transactions on Mobile Computing (TMC), Vol. 20, No. 3, pp. 1186-1197, 2021. 

For a complete list of publications, visit Dr. Lu’s  Google Scholar Citations