Ahmed M. A. Sayed, also known as Ahmed M. Abdelmoniem, is a Lecturer (Assistant Professor) at Queen Mary University of London (QMUL). He directs the BDS Programme and leads the SAYED Systems Group, focusing on building Scalable, Advanced Yet Efficient Distributed Systems of the Future. Ahmed holds a Ph.D. in Computer Science and Engineering from Hong Kong University of Science and Technology (HKUST) and has extensive experience in research, teaching, and leadership roles in academia and industry.
Author Metrics:
Google Scholar Profile
Ahmed’s research contributions are notable, with a strong publication record and involvement in leading conferences and journals in the field of computer science. He has received honors such as the Hong Kong PhD Fellowship (HKPFS) award and has contributed to various research projects funded by prestigious institutions.
Citations: Ahmed has amassed a total of 1178 citations for his scholarly work, demonstrating the recognition and relevance of his research within the academic community.
h-index: With an h-index of 24, Ahmed has 24 papers that have each received at least 24 citations, indicating both the productivity and impact of his research output.
i10-index: Ahmed’s i10-index, which measures the number of publications with at least 10 citations, stands at 35. This metric further emphasizes the significance and visibility of his work, as it reflects the number of highly cited papers he has authored.
Education:
Ahmed completed his Ph.D. in Computer Science and Engineering at HKUST, where his thesis focused on improving the performance of TCP applications in public cloud networks. Prior to his Ph.D., he earned his M.Sc. in Computer Science with distinction from Assiut University, Egypt, and his B.Sc. in Computer Science with honors.
Research Focus:
Ahmed’s research interests encompass Systems for ML, Federated & Distributed Machine Learning, Computer Networks, and Wireless Networks. He leads projects related to ML Congestion Control, Moderation in Decentralized Social Networks, and Machine Learning Architecture for Task-based Information Transfer.
Professional Journey:
Ahmed’s professional journey includes roles as a Research Scientist and Post-Doctoral Research Fellow at King Abdullah University of Science and Technology (KAUST), Assistant Professor at Assiut University, Senior Researcher at Huawei Technologies, and currently as a Lecturer at QMUL.
Honors & Awards:
Throughout his academic career, Ahmed has received prestigious honors and awards, including the HKPFS award, travel grants, and recognition for his research contributions. He has also been actively involved in professional societies and has completed professional development courses.
Publications Noted & Contributions:
Ahmed has made significant contributions to the field of computer science through his research publications, including his Ph.D. thesis and M.Sc. thesis. His work has been published in leading conferences and journals, contributing to the advancement of knowledge in areas such as distributed systems, machine learning, and computer networks.
“Towards energy-aware Federated Learning via collaborative computing approach”
- Authors: A Arouj, AM Abdelmoniem
- Published in: Computer Communications
- This paper explores the concept of energy-aware Federated Learning using a collaborative computing approach, aiming to optimize energy consumption in distributed machine learning systems.
“Decentralised Moderation for Interoperable Social Networks: A Conversation-based Approach for Pleroma and the Fediverse”
- Authors: V Agarwal, A Raman, N Sastry, AM Abdelmoniem, G Tyson, I Castro
- Published in: arXiv preprint arXiv:2404.03048
- The research proposes a decentralized moderation approach for interoperable social networks, focusing on Pleroma and the Fediverse, with an emphasis on conversation-based moderation techniques.
“FLOAT: Federated Learning Optimizations with Automated Tuning”
- Authors: AF Khan, AA Khan, AM Abdelmoniem, S Fountain, AR Butt, A Anwar
- Presented at: Nineteenth European Conference on Computer Systems (EuroSys’ 24)
- This paper presents FLOAT, a system for optimizing Federated Learning through automated tuning, aiming to enhance the efficiency and performance of distributed machine learning models.
“Dimensioning the pending interest table in content-centric networks”
- Authors: AJ Abu, B Bensaou, AM Abdelmoniem
- Published in: Future Generation Computer Systems
- The study focuses on dimensioning the pending interest table in content-centric networks, providing insights into optimizing resource allocation and management in such networks.
“Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications”
- Authors: H Alrubayyi, MS Alshareef, Z Nadeem, AM Abdelmoniem, M Jaber
- Published in: Future Internet
- This research investigates security threats and potential solutions emerging from the intersection of Artificial Intelligence (AI) and Internet of Things (IoT), particularly focusing on IoMT (Internet of Medical Things) and IoET (Internet of Energy Things) applications.
Research Timeline:
Ahmed’s research timeline spans from his undergraduate and graduate studies to his current role as a lecturer and researcher. He has been involved in various research projects, collaborations, and supervisory roles, contributing to advancements in distributed systems, machine learning, and network optimization.
Collaborations and Projects:
Ahmed has collaborated with renowned institutions and researchers worldwide on projects related to ML Congestion Control, Federated Learning, Network Optimization, and Distributed Systems. His involvement in interdisciplinary collaborations has led to innovative solutions and advancements in the field of computer science.
