Ahmed M. A. Sayed, Associate Professor
Dr. Ahmed (aka. Ahmed M. Abdelmoneim) is a member of the school of Electronic Engineering and Computer Science at Queen Mary University of London. He has a PhD in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) advised by Prof. Brahim Bensaou. He held the positions of Senior Researcher at Future Networks Lab, Huawei Research, Hong Kong and Research Scientist at SANDS Lab, KAUST, Saudi Arabia. His early research involved optimizing networked systems to improve the performance of applications in wireless and data center networks and proposing efficient and practical systems for distributed machine learning. His current research focus involves designing and prototyping Networked and Distributed Systems of the Future, in particular, he is interested in developing methods and techniques to improve and enhance the performance of networked and distributed systems. He is currently focusing on developing scalable and efficient systems supporting distributed machine Learning (esp., distributed privacy-preserving machine learning aka. Federated Learning).
Songyuan Li, Postdoctoral Research Associate
Songyuan Li completed the Ph.D. in Computer Science from the University of Exeter, U.K., in 2025. Before that, he obtained the M.Eng. and B.Eng. degrees in Computer Science and Technology from Beijing University of Posts and Telecommunications, China, in 2018 and 2020, respectively. He has nearly 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: (1) Distributed machine learning (e.g., federated learning and distributed data analytics); (2) Edge Intelligence (e.g., AIoT and resource-efficient ML model inference/training); (3) Generative AI (e.g., large language models, multimodal models, and mixture-of-experts models); and (4) 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 Cognitive Communications and Networking, the IEEE Transactions on Network and Service Management, IEEE ICWS, IEEE SCC, IEEE ISPA, etc. His research is supported by EU Horizon, UK EPSRC, NSFC (China) and National Key R&D Program (China). More information can be found at https://songyuanli.github.io.
Wai Fong (Herman) Tam, PhD student
Wai Fong (Herman) Tam is an aspiring cybersecurity analyst and Ph.D. candidate with a background in computer science and a specialization in information security. With a master’s degree distinguished by a research project in network resource optimization, Herman is set to bring his analytical skills and technical knowledge to the cutting-edge KUber project at Queen Mary University of London. During his professional tenure, Herman has accrued practical experience in IT and network security, coupled with a focus on developing machine learning-based security solutions. He is proficient in programming languages, including Python and C++, and is keen to leverage these skills within distributed machine learning. As he joins the KUber project, Herman will contribute to developing a novel distributed architecture designed to facilitate the exchange of acquired knowledge among learning entities at scale. This project aligns with his interest in the application of machine learning in networked systems and his ambition to be part of a groundbreaking effort that addresses the challenges of decentralized edge learning. Herman’s role in the KUber initiative will leverage his expertise in cybersecurity, machine learning, and network optimization to enhance collaborative learning and the use of AI/ML methods in everyday applications.