Songyuan Li (he/him)

Songyuan Li is currently a Postdoctoral Researcher in Machine Learning Systems at Queen Mary University of London (QMUL) since October 2025. He collaborates with Dr. Ahmed M. Abdelmoneim in the Scalable Adaptive Yet Efficient Distributed (SAYED) Systems Group on the EPSRC/UKRI-funded project KUber (Knowledge Delivery System for Machine Learning at Scale, £650K). He completed his Ph.D. in Computer Science at the University of Exeter, U.K, in 2025. Before that, he received the B.Eng. and M.Eng. degrees in Computer Science and Technology from the Beijing University of Posts and Telecommunications (BUPT), China, in 2018 and 2021, respectively.

He has a decade of research experience in distributed systems and networks. His research spans the fields of artificial intelligence (AI) systems, cloud computing, edge computing, and the Internet of Things (IoT). Currently, he focuses on advancing the quality and efficiency of distributed intelligence, including a board of key topics:
▪ Distributed machine learning (e.g., federated learning and distributed data analytics)
▪ Edge Intelligence (e.g., AIoT and resource-efficient ML model inference/training)
▪ Generative AI (e.g., large language models, multimodal models, and mixture-of-experts models)
▪ Edge/cloud computing (e.g., Quality-of-Service optimization and resource management)

Thus far, he has published several articles in international journals and conference proceedings, including the IEEE Transactions on Mobile Computing, IEEE Transactions on Cognitive Communications and Networking, IEEE Transactions on Network and Service Management, IEEE ICWS, IEEE SCC, etc. His research is supported by EU Horizon, UK EPSRC, UKRI, NSFC (China) and National Key R&D Program (China).