The Intelligent Communication Lab, housed within the State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) at the University of Macau, is dedicated to pushing the frontiers of research and innovation in wireless communications and networking. We have a dual research focus: one dedicated to uncovering fundamental insights, and the other focused on crafting practical solutions with real-world impact. To achieve these goals, we employ a blend of traditional techniques like signal processing and interdisciplinary methods such as data science, pioneering cutting-edge technologies that enhance the reliability and efficiency of various wireless communication systems.

About the Supervisor

I am an Assistant Professor at the State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau, a Visiting Researcher at the Department of Electrical and Electronic Engineering (EEE), Imperial College London, and a Series Editor of IEEE Communication Magazine (ComMag) for the track “Artificial Intelligence and Data Science for Communications”.

Please contact me by:

University of Macau: ylshao@um.edu.mo

Imperial College London: y.shao@imperial.ac.uk

IEEE Communications Magazine: ylshao@ieee.org

Education

• Ph.D. in Information Engineering, Chinese University of Hong Kong, Aug. 2016 – Dec. 2020.
• B.E. and M.E. in Communication Engineering, Xidian University, Sept. 2009 – Jan. 2016.

Work experience

• University of Exeter, Nov. 2022 – Aug. 2023
  • Lecturer in Information Processing, Department of Engineering
• Imperial College London, Jan. 2021 – Nov. 2022
  • Research Associate, Department of Electrical and Electronic Engineering
• Massachusetts Institute of Technology, Sept. 2018 – Mar. 2019
  • Visiting Scholar, Claude E. Shannon Communication and Network Group
• Institute of Network Coding, Mar. 2015 – July 2016
  • Research Assistant

Research Interests

• Fundamentals of wireless communications
  • Tools: signal processing, matrix theory, real analysis, statistical inference.
• Data science and machine learning
  • Tools: deep learning, variational Bayesian methods, graph signal processing.
• Networking and stochastic control
  • Tools: Markov decision process theory, reinforcement learning, optimization.

Awards

• IEEE Wireless Communications and Networking Conference 2024, Best Paper Award.
• IEEE International Conference on Communications 2023, Best Paper Award.
• International Telecommunication Union (ITU) AI/ML in 5G Challenge 2021, ranked third in problem “Federated learning for spatial reuse” and nominated as a finalist in the Grand Challenge Finale.
• Global scholarship programme for research excellence, 2019.
• Overseas research attachment programme, 2018.

Professional Services

• IEEE Communications Magazine, Series Editor.
• IEEE Communication Society flagship conferences, Session chair and TPC member.
• 5G Academy Italy 2022, Guest Lecturer.
• IEEE Information Theory Society Bangalore Chapter, Invited Speaker.

Selected Journal Publications

• Y. Shao. DEEP-IoT: Downlink-Enhanced Efficient-Power Internet of Things, https://arxiv.org/abs/2403.00321, 2024.

• Y. Shao, Q. Cao, and D. Gunduz. A Theory of Semantic Communication, https://arxiv.org/abs/2212.01485, 2024.

• Y. Shao, C. Bian, L. Yang, Q. Yang, Z. Zhang, D. Gunduz, Point Cloud in the Air, https://arxiv.org/abs/2401.00658, 2024.

• Y. Shao, S. Liew and D. Gunduz. Denoising noisy neural networks: A Bayesian approach with compensation, IEEE Transactions on Signal Processing, 2023.

• Y. Shao, Y. Cai, T. Wang, Z. Guo, P. Liu, J. Luo, D. Gunduz. Learning-based autonomous channel access in the presence of hidden terminals, IEEE Transactions on Mobile Computing, 2023.

• Y. Shao, D. Gunduz and S. Liew. Bayesian over-the-air computation, IEEE Journal on Selected Areas in Communications, vol. 41, no. 3, pp. 589-606, 2023.

• Y. Shao, D. Gunduz and S. Liew. Federated edge learning with misaligned over-the-air computation,” IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 3951-3964, 2022.

• Y. Shao, D. Gunduz. Semantic communications with discrete-time analog transmission: a PAPR perspective,” IEEE Wireless Communication Letter, 2022.

• Y. Shao. Goal-oriented communication system redesign for wireless collaborative intelligence, IEEE Multimedia Communication Technical Committee – Frontiers, 2022.

• Y. Shao, Q, Cao, S. Liew, and H. Chen. Partially observable minimum-age scheduling: the greedy policy, IEEE Transactions on Communications, vol. 70, no. 1, pp. 404-418, 2021.

• Y. Shao, S. Liew, H. Chen, Y. Du. Flow sampling: network monitoring in large-scale software-defined IoT networks, IEEE Transactions on Communications, vol. 69, no. 9, pp. 6120-6133, 2021.

• Y. Shao and S. Liew. Flexible subcarrier allocation for interleaved frequency division multiple access, IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7139-7152, 2020.

• Y. Shao, A. Rezaee, S. Liew, and V. Chan. Significant sampling for shortest path routing: a deep reinforcement learning solution, IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2234–2248, 2020.

• Y. Shao, S. Liew, and J. Liang. Sporadic ultra-time-critical crowd messaging in V2X, IEEE Transactions on Communications, vol. 69, no. 2, pp. 817-830, 2020.

• Y. Shao, S. Liew, and T. Wang. AlphaSeq: sequence discovery with deep reinforcement learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3319–3333, 2019.

• Y. Shao, S. Liew, and L. Lu. Asynchronous physical-layer network coding: symbol misalignment estimation and its effect on decoding, IEEE Transactions on Wireless Communications, vol. 16, no. 10, pp. 6881–6894, 2017.