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).
Kevin (Qilei) Li, Postdoctoral Research Assistant
Kevin Li is a Ph.D. candidate in Computer Science nearing graduation at Queen Mary University of London, under the supervision of Prof. Shaogang (Sean) Gong. He previously earned an M.S. degree from Sichuan University in 2020. From June 2022 to April 2024, he worked as a machine learning scientist at Veritone Inc, where he focused on developing a scalable person search framework for retrieving individuals at different locations and times, as captured by various cameras. His current research interests lie in privacy-aware multimodal machine learning, with a particular emphasis on learning domain-invariant knowledge representation from multimodal data captured in diverse environments. His research outcome has been recognized as ESI Highly Cited Paper (Top 1%). Additionally, he serves as an evaluator for the ELLIS PhD Program, and as a reviewer for numerous journals and conferences, including IEEE TPAMI, IEEE TIP, IEEE TNNLS, IEEE TCSVT, IEEE TAI, and Information Fusion.
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.