• Dr. Ahmed M. A. Sayed, Associate Professor

    Dr. Ahmed (aka. Ahmed M. Abdelmoneim) is an Associate Professor in 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.

  • Bradley Aldous, PhD student

    Brad is string with the group on Sept 2023 as a PhD student. He holds 4-year MSci degree in Astrophysics from Queen Mary University of London, achieving a Distinction. Whilst conducting the research element for his project in the final year, he gradually gravitated towards being more centred around programming. He then obtained an MSc in Data Science and Artificial Intelligence from Queen Mary University of London with Distinction. He is interested in the intersection of fields of deep learning and audio.

  • 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.

  • Leon Tabaro, PhD student

    Leon Tabaro joined the group in May 2025 as a PhD student. He holds a BEng in Aeronautical Engineering and an MSc in Artificial Intelligence from Loughborough University, where he focused on deep reinforcement learning applications to quantitative trading. His research interests lie at the intersection of Natural Language Processing and Machine Learning Optimization, with a strong emphasis on improving model adaptability, efficiency, and scalability. He is particularly interested in designing deep learning architectures that enable efficient training and inference, especially in resource-constrained environments. His current work explores topics on efficient transformer training and inference, sequence models with long-range memory, and structured sparsity for compact deep learning models. His goal is to make AI accessible to everyone. Regardless of language, nation or compute.

  • Xiaolong Jia, PhD student

    Xiaolong joined the group in September 2025. He obtained his Master of Engineering degree from Chongqing University, where he focused on applying generative artificial intelligence to interior layout design using methods such as generative adversarial networks and graph neural networks. His current research focuses on resource-efficient training and optimization of large models, with emphasis on adaptive parallel strategies to improve scalability, load balancing, and communication efficiency in distributed environments. His research interests include distributed training, large models, and generative AI.

  • Yemisi Bidemi Oyeleke, PhD student

    Yemisi previously worked at the BT Innovation Lab as an Applied Research Specialist in Quality of Experience and Self-Learning Network. Prior to this, she worked as a software developer and User Experience Analyst at BT Digital. She received her master's degree in advanced computing from the University of Leicester in the United Kingdom. Her primary focus is user experience, software engineering, and machine learning.

  • Qianqian Zhang, Visiting PhD student

    Qianqian joined the team from September 2025 to March 2026. She is a visiting PhD student funded by the China Scholarship Council. At the same time, she is a PhD student at the University of Chinese Academy of Sciences (from September 2021 to June 2026). Before that, she worked as a research intern at Tsinghua University (from June 2024 to January 2025). Her main research interests include Large Visual Model, Multimodal Fusion, Object Detection, Model Lightweighting, Model Efficient Deployment, and Video Compression. She is looking for a postdoctoral position and related funding opportunities at universities ranked in the top 100 of the QS World University Rankings.

  • Amna Arouj, Remote Research Intern

    Amna is a graduate of the computer engineering program at the National University of Sciences and Technology (NUST), Pakistan and is a bronze medalist. She worked at NUST as a Lab research assistant in a computer science lab for about two years. Previously, she did a summer research internship at Purdue University with Dr. Muhammad Shahbaz where she broadly explored the areas of SDN, Programmable Networks, and Edge Cloud.

  • Yomna M. Abdelmoniem, Remote Research Intern

    Yomna received the B.Sc degree in Information Technology from the Faculty of Computers and Information, Assiut University, Assuit, Egypt in 2013. She is working as Teaching Assistant at the Information Technology Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt, since 2013, where she is currently pursuing her MSc degree. Her research interests are Computer Networks, Cloud/Edge Computing, and Resource Allocation/Scheduling.

  • Jyoti Prakash Sahoo, Remote Research Collaborator

    Jyoti is continuing on a Graduate Research position at NTUST, Taiwan. He is an IEEE Senior Member and works as an assistant professor with the Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, India. He has expertise in the field of cloud computing and machine learning. He is with several journals and conferences as an editorial or reviewer board member.

  • Amin Keshavarzi, Remote Research Collaborator

    Amin has recently graduated with Ph.D. in computer engineering from Iran. Currently, he works as a lecturer at the computer engineering department of Marvdasht IAU in Iran. He has expertise in IoT/edge/cloud continuum as well as machine learning and deep learning. His research has resulted in 2 nationally registered patents, 3 international research projects, 11 ISI-indexed journal papers (almost Q1 and Q2), 2 Springer book chapters, 5 conference papers, 2 translated books, and 6 national research projects.

    • Alumni

  • Qilei Li, Postdoctoral Research Assistant

    Qilei Li is now an Associate Professor at Central China Normal University (CCNU), China. Until the end of May 2025, he was a post-doc researcher at SAYED Systems Group. Prior to that, he has a PhD in Computer Science from Queen Mary University of London, supervised by 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. He is an associate editor for the Expert Systems journal.

  • Varun Kukreti, Remote Research Intern

    Pantelis joined the group during the period of May 2023 to Sept 2023. Varun is an undergradute student who is pursuing B.Tech. in Electrical Engineering from National Institute of Technology Hamirpur (India). Previously, he was a Summer Research Intern at IIT Hyderabad (India) and a Project Intern at IISER Tiruvananthapuram (India). His main focus lies in the field of machine learning and deep learning. He is interning during Summer of 2023 with SAYED Sys Group and is working on projects related to Federated Learning and Distributed Music Generation.

  • Pantelis Papageorgiou, Remote Research Intern

    Pantelis joined the group during the period of Nov 2021 to Sept 2023. He is currently pursing his Masters degree at University of Oxford. Pantelis received a B.S in Computer Science from National And Kapodistrian University of Athens (NKUA). His Bachelor thesis was on the topic of unsupervised discovery of interpretable directions in the latent space of GANs. His main focus lies in the field of deep learning and computer vision. He is also passionate about deploying solutions for real-world applications, while utilising federated learning techniques.