[Dec 2023] Our paper "Backlogged Bandits: Cost-Effective Learning for Utility Maximization in Queueing Networks" got accepted to the IEEE INFOCOM 2024 (Acceptance rate: 19.6%).
[Dec 2022] Our paper "Constrained Bandit Learning with Switching Costs for Wireless Networks" got accepted to the IEEE INFOCOM 2023 (Acceptance rate: 19.2%).
[Nov 2022] Congratulations to Mahdi on receiving the IEEE Globecom 2022 student travel grant.
[May 2022] MASc student Mahdi was invited to speak at Flower Summit 2022 on Communication-efficient Federated Learning with Autoencoder Compression.
[March 2022] Congratulations to undergraduate student Bill Zhang on receiving a Charles Allan Thompson Award for Summer 2022.
[March 2022] Congratulations to Juaren and Mahdi on receiving the IEEE INFOCOM 2022 student conference grant.
[Feb 2022] Our paper "Multi-frame Scheduling for Federated Learning over Energy-Efficient 6G Wireless Networks" got accepted to the IEEE INFOCOM 2022 workshop on Pervasive Network Intelligence for 6G Networks.
[Dec 2021] Our paper "Learning from Delayed Semi-Bandit Feedback under Strong Fairness Guarantees" got accepted to the IEEE INFOCOM 2022 (Acceptance rate: 19.9%).
[Dec 2021] PhD student Juaren Steiger received Mitacs Globalink Research Award. Congratulations to Juaren.
Research
Foundations: networking, computing, and learning for wireless networks
Applications: IoT, 5G and beyond, connected vehicles
Alastor Liu (Undergraduate 2022, Graduate student at the University of Toronto)
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%]
L. Ma, N. Cheng, C. Zhou, X, Wang, N. Lu, N. Zhang, K. Aldubaikhy, and A. Alqasir, "Dynamic Neural Network-Based Resource Management for Mobile Edge Computing in 6G Networks," IEEE Transactions on Cognitive Communications and Networking (TCCN), accepted.
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.
N. Lu, Y. Zhou, C. Shi, N. Cheng, L. Cai, and B. Li, "Planning while flying: a measurement-aided dynamic planning of drone small cells," IEEE Internet of Things Journal (IoTJ), Vol. 6, No. 2, pp. 2693-2705, 2018.
N. Lu, B. Ji, and B. Li, "Age-based Scheduling: Improving Data Freshness for Wireless Real-Time Traffic," Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Los Angeles, California, USA, June 2018. [Acceptance rate: 16.9%]
