Academic Qualifications

Ph.D. in Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong

B.Eng. in Information and Communication Engineering, Zhejiang University, China

Teaching Areas

Communication Networks

Introduction to Artificial Intelligence

Research Areas

Wireless Communications and Networking

Mobile Edge Intelligence

Integrated Sensing, Communication, and Computation

Distributed and Federated Learning

Smart Internet of Things

Professional Services

Editor, IEEE Wireless Communications Letters

Associate Editor, EURASIP Journal on Wireless Communications and Networking

Associate Editor, HKIE Transactions

Working Experience

Jan. 2025 ~ present, Assistant Professor, School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau

Jul. 2023 ~ Jan. 2025, Research Assistant Professor, Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong

May 2020 ~ Jun. 2023, Research Assistant Professor, Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong

May 2019 ~ Apr. 2020, Senior Researcher, Theory Lab, 2012 Labs, Huawei Tech. Investment Co. Ltd., Hong Kong

Oct. 2017 ~ Apr. 2019, Senior Engineer/Lead Engineer, Communications Technologies Division, Hong Kong Applied Science and Technology Research Institute Co. Ltd., Hong Kong

Academic Publication

Journal Articles (Past Five Years)

1.      X. Ye, Y. Mao, X. Yu, and L. Fu, “Intelligent omni-surface-aided integrated sensing and communications based on deep reinforcement learning with knowledge transfer,” IEEE Trans. Wireless Commun., to appear.

2.      Z. Fu, J. Liu, Y. Mao, L. Qu, L. Xie, and X. Wang, “Energy-efficient UAV-assisted federated learning: Trajectory optimization, device scheduling, and resource management,” IEEE Trans. Netw. Service Manag., to appear.

3.      W. Zhuang, X. He, Y. Mao, and J. Liu, “UAV-enabled wireless networks for integrated sensing and learning-oriented communication,” IEEE Wireless Commun. Lett., vol. 14, no. 2, pp. 340-344, Feb. 2025.

4.      L. Qu, Y. Mao, S. Song, and C. Y. Tsui, “Energy-efficient channel decoding for wireless federated learning: Convergence analysis and adaptive design,” IEEE Trans. Wireless Commun., vol. 23, no. 11, pp. 17222-17235, Nov. 2024.

5.      Y. Mao, X. Yu, K. Huang, Y.-J. A. Zhang, and J. Zhang, “Green edge AI: A contemporary survey,” Proc. IEEE, vol. 112, no. 7, pp. 880-911, Jul. 2024.

6.      Z. Li, Z. Lin, J. Shao, Y. Mao, and J. Zhang, “FedCiR: Client-invariant representation learning for federated non-IID features,” IEEE Trans. Mob. Comput., vol. 23, no. 11, pp. 10509-10522, Nov. 2024.

7.      Z. Li, Y. Sun, J. Shao, Y. Mao, J. H. Wang, and J. Zhang, “Feature matching data synthesis for non-IID federated learning,” IEEE Trans. Mob. Comput., vol. 23, no. 10, pp. 9352-9367, Oct. 2024.

8.      Y. Sun, Z. Lin, Y. Mao, S. Jin, and J. Zhang, “Channel and gradient-importance aware device scheduling for over-the-air federated learning,” IEEE Trans. Wireless Commun., vol. 23, no. 7, pp. 6905-6920, Jul. 2024.

9.      W. Zhuang, Y. Mao, H. He, L. Xie, S.H. Song, Y. Ge, and Z. Ding, “Approximate message passing-enhanced graph neural network for OTFS data detection,” IEEE Wireless Commun. Lett., vol. 13, no. 7, pp. 1913-1917, Jul. 2024.

10.  Y. Sun, Y. Mao, and J. Zhang, “MimiC: Combating client dropouts in federated learning by mimicking central updates,” IEEE Trans. Mob. Comput., vol. 23, no. 7, pp. 7572-7584, Jul. 2024.

11.  Y. Sun, J. Shao, Y. Mao, S. Li, and J. Zhang, “Stochastic coded federated learning: Theoretical analysis and incentive mechanism design,” IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 6623-6638, Jun. 2024.

12.  X. Bian, Y. Mao, and J. Zhang, “Grant-free massive random access with retransmission: Receiver optimization and performance analysis,” IEEE Trans. Commun., vol. 72, no. 2, pp. 786-800, Feb. 2024.

13.  X. Bian, Y. Mao, and J. Zhang, “Joint activity detection, channel estimation, and data decoding for grant-free massive random access,” IEEE Internet Things. J., vol. 10, no. 16, pp. 14042-14057, Aug. 2023.

14.  Y. Sun, J. Shao, Y. Mao, J. H. Wang, and J. Zhang, “Semi-decentralized federated edge learning with data and device heterogeneity,” IEEE Trans. Netw. Service Manag., vol. 20, no. 2, pp. 1487-1501, Jun. 2023.

15.  J. Shao, Y. Mao, and J. Zhang, “Task-oriented communication for multi-device cooperative inference,” IEEE Trans. Wireless Commun., vol. 22, no. 1, pp. 73-87, Jan. 2023.

16.  R. Dong, Y. Mao, and J. Zhang, “Resource-constrained edge AI with early exit prediction,” J. Commun. Inf. Netw., vol. 7, no. 2, pp. 122-134, Jun. 2022.

17.  J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Sel. Areas Commun., vol. 40, no. 1, pp. 197-211, Jan. 2022.

Research Grants

2022.10 -- 2025.4    Co-I, Channel state information-learning-based passenger counting system on public transport vehicles (Smart Traffic Fund, Transport Department, HKSAR)

2021.7 -- 2024.10    PI, Edge-assisted Deep Learning Inference for Smart Internet-of-Things (Hong Kong Polytechnic University)

Professional Society Membership

Senior Member, IEEE