(Note: A more frequently updated homepage can be found at https://lynshao.github.io/Lab.github.io/)
YULIN SHAO
Assistant Professor, State Key Laboratory of Internet of Things for Smart City, University of Macau.
CONTACT (联系方式):
• University of Macau: ylshao@um.edu.mo
• Imperial College London: y.shao@imperial.ac.uk
• IEEE Communication Society: ylshao@ieee.org
ACADEMIC QUALIFICATIONS (学习经历):
• Ph.D. in Information Engineering, Chinese University of Hong Kong (Aug. 2016 – Dec. 2020).
• B.E. and M.E. (Hons.) in Communication and Information Systems, Xidian University (Sept. 2009 – Jan. 2016).
APPOINTMENTS (工作经历):
• Imperial College London (Nov. 2022 – present)
Visiting Researcher at the Department of Electrical and Electronic Engineering.
• University of Exeter (Nov. 2022 – Aug. 2023)
Lecturer (assistant professor) in Information Processing at the Department of Engineering.
• Imperial College London (Jan. 2021 – Nov. 2022)
Research Associate at the Department of Electrical and Electronic Engineering.
• Massachusetts Institute of Technology (Sept. 2018 – Mar. 2019)
Visiting Scholar at the Claude E. Shannon Communication and Network Group.
• Institute of Network Coding (Mar. 2015 – July 2016)
Research Assistant at the Institute of Network Coding.
RESEARCH INTERESTS (研究兴趣):
• Fundamentals of wireless communications.
Tools: signal processing, matrix theory, real analysis, statistical inference.
• Data science and machine learning.
Tools: deep learning, variational Bayesian methods, reinforcement learning, graph signal processing.
• Networking and stochastic control.
Tools: Markov decision process theory, reinforcement learning, optimization.
AWARDS (所获奖项):
• 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.
• Oversea research attachment programme 2018.
PROFESSIONAL SERVICES (行业服务):
• IEEE Communications Magazine, Series Editor for the track “Artificial Intelligence and Data Science for Communications”.
• Session chair and technical program committee (TPC) member for IEEE flagship conferences.
SELECTED PUBLICATIONS (部分期刊论文):
• Y. Shao, E. Ozfatura, A. Perotti, B. Popovic, and D. Gunduz. AttentionCode: ultra-reliable feedback codes for short-packet communications, IEEE Transactions on Communications, 2023.
• 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.
PUBLICATIO
