Dr Ahmed M. A. Sayed, PhD, MPhil, BSc, FHEASenior Lecturer (Associate Professor), Director of MSc Data Science Programme, DERI FellowEmail: ahmed.sayed@qmul.ac.ukRoom Number: ENG 153a, Engineering BuildingWebsite: http://www.eecs.qmul.ac.uk/~ahmed/Twitter: @ahmedcs982ProfileTeachingResearchPublicationsSupervisionPerformanceGrantsProfileDr. Ahmed M. A. Sayed (aka. Ahmed M. Abdelmoniem) is a Senior Lecturer (Research & Teaching), the equivalent of Associate Professor, at the School of Electronic Engineering and Computer Science at Queen Mary University of London, UK. He is also the Director of the MSc Big Data Science Programme. He leads the SAYED Systems Group and works on various topics related to Distributed Systems, Systems for ML & ML for Systems, Federated Learning, Edge/Cloud Computing, Congestion Control, and Software-Defined Networking (SDN). In 2017, he earned a Ph.D. degree in Computer Science and Engineering under the supervision of Brahim Bensaou from the Hong Kong University of Science and Technology (HKUST) ([Ph.D. Thesis PDF]), where he worked to enhance the performance of TCP applications in Data Center Networks. He completed with Distinction both the B.Sc. and M.Sc. degrees (Coursework & Research) in Computer Science from Assiut University (AUN), Egypt in 2007 and 2012, respectively. Before joining QMUL, he was a research scientist at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, working with Marco Canini in the SANDS Lab on problems related to distributed ML systems. Before that, he worked as a Senior Researcher at Huawei's Future Network Research Lab on the design and architecture of Application-Driven Networking (ADN). He also previously held the position of Assistant Professor at Assiut University, Egypt. His research spans inter-related disciplines of computer science and engineering with a focus on system design and optimization for machine learning systems (training and inference efficiency, distributed ML, federated learning), distributed systems (architecture design, performance analysis, resource allocation, algorithmic optimization), computer networks (traffic engineering, congestion control, performance optimization, software-defined networking), and wireless networks (routing in mobile ad-hoc and wireless sensor networks). He is always looking for bright and talented students and researchers who are passionate about researching to study and solve real-world problems. If you find the above topics intriguing, please get in touch by dropping him an email or his personal webpage for any announced opportunitiesTeachingECS640U/ECS640A/ECS765P Big Data Processing Big Data Processing covers the new large-scale programming models that allow to easily create algorithms that process massive amounts of information with a cluster of computer nodes. These platforms hide the complexity of coordinating complex parallel computations across the cooperating nodes, instead providing developers with a high-level programming model. The module is based on the MapReduce programming model. Lectures explain how multiple data analysis algorithms can be expressed under this model, and executed automatically over clusters of machines. The module also covers the internal mechanisms that a MapReduce framework uses to coordinate and execute the job among the infrastructure. Finally, additional related topics in the area of Big Data, such as alternative large-scale processing platforms, NoSQL data stores, and Cloud Computing execution infrastructure are presented. In addition to the lectures, weekly lab sessions and coursework exercises present multiple applications where real-world datasets are analysed using platforms such as Hadoop. ECS637U/ECS757P Digital Media and Social Network Online social networks and digital media services such as Facebook, twitter, Flickr, YouTube are changing the way we interact with the Internet and receive our news, content and recommendations. In this module, you will be introduced to the concepts of measurement, analysis, usability and privacy aspects of OSNs. The module will bring together a number of studies from different measurement studies on the topic, designs for new systems, and the directions that such networks are taking with the new digital media plans. You will develop a deep understanding and analysis approach to learning specifically about Social Media and its properties. Undergraduate TeachingECS637U/ECS757P Digital Media and Social Network ECS640U/ECS640A Big Data ProcessingPostgraduate TeachingECS765P Big Data ProcessingResearchResearch Interests:See Ahmed M.A. Sayed’s research profile pages including details of research interests, publications, and live grants.Examples of research funding:Starting 2024: QMUL - Principal Investigator of UKRI-EPSRC funded New Investigator Award (NIA) project on Knowledge Delivery System for Machine Learning at Scale (KUber) - 652,000 GBP 2022 - Now: QMUL - CoI of UKRI-funded project on Moderation in Decentralised Social Networks (DSNmod) - 81,000 GBP 2022 - Now: HKUST - CoI of GRF-funded project on ML methods for Congestion Control in SDN-based Networks - 600,000 HKD 2021 - Now: KAUST - CoI of CRG- funded project on Machine Learning Architecture for Task-based Information Transfer - 400,000 USDPublicationsClick HERE for Ahmed M.A. Sayed’s Google Scholar Full List of PublicationsSupervision Sept 2024 - Now: Herman Tam - pursuing PhD in Computer Science (Focus on KUber - Knowledge Delivery System for Machine Learning at Scale) - Funded by QMUL via Project's Flexible Research Fund Apr 2024 - Now: Qilei Li - Post-Doctoral Research Assistant (PDRA) Leading work on KUber - Knowledge Delivery System for Machine Learning at Scale Project - Funded by UKRI/EPSRC Sept 2023 - Now: Bradely Aldous - pursuing PhD in Computer Science (Focus on Accelerated Distributed Machine Learning Systems) - Funded by EPSRC AIM CDT Sept 2021 - Now: Yemisi Oyelek - pursuing EngD in Computer Science (Focus on Mitigating Video Degradation over Networks via Digitial Twin) - Funded by the EPSRC-CDT for Data-Centric Engineering --------------------------------------------- Past Supervision ----------------------------- 2023 PhD - QMUL, Efficient Machine Learning on Decentralized Data - Funded by CONACyT/IPN/UABC/CIMAV/UDLAP initiative 2023 PhD - QMUL, Federated ML for enhancing security and privacy of IoT networks - Self-Funded 2022 Intern - QMUL, Energy-Aware Methods for Federated Learning on Battery-Powered Devices. 2021 MS/PhD - KAUST, Mitigating Device Heterogeneity in Federated Learning via Asynchronous Stale Updates. 2021 MS/PhD - KAUST, Prioritizing Participant Selection for Efficient Federated Learning. 2021 MS/PhD - KAUST, Identifying the Limits of Gradient Sparsification Methods for Distributed Machine Learning. 2020 Research Student Interns - KAUST, Study of Fairness and Bias in Federated Learning settings. 2020 Research Student Interns - KAUST, An Efficient compression technique to reduce Communication in Distributed Deep Learning. 2019 MS/PhD - KAUST, Survey and Empirical Analysis of Compressed Communication for Distributed Deep Learning. 2019 MS/PhD - KAUST, Theoretical and Empirical Analysis of Layerwise and Whole-Model Compressed Communication Methods in Distributed Machine Learning. 2019 Research Student Intern - KAUST, Energy-Efficiency of Hardware Offloading: Case- Study on Distributed Machine Learning. 2019 Research Student Intern - KAUST, Scaling Distributed Machine Learning with In-Network Aggregation using Smart NICs. 2019 Research Student Intern - KAUST, Accelerating Distributed Deep Learning with Adaptive Compression and Communication Scheduling. 2018 PhD Research Student Intern - Huawei Research, Leveraging Programmable Data Plane to Accelerate Distributed Applications. 2018 PhD Research Student Intern - Huawei Research, An Online Learning Multi-Path Selection Framework for Multi-path Transmission Protocols. 2018 Research Student Intern - Huawei Research, Implementation of an SDN-based Fast-Slow Control System to Realise an Operational Prototype of the Application-Driven Networking (ADN) Framework. 2007-2013 FYPs UG Students - Assiut University, Management System for controlling Wireless Access Points, HoneyPot Server Application, WiiMote Body Tracking & Robot Control System, Steganography Application to hide data in images and videos, Remote Desktop Control using Mobile Phones, Mobile Application in Traffic Service, Tourist Heaven a tourist social networking application and Egyptian tourism company web system. PerformanceStarting 2024: QMUL - Principal Investigator of UKRI-EPSRC funded New Investigator Award (NIA) project on Knowledge Delivery System for Machine Learning at Scale (KUber) - 652,000 GBP 2022 - Now QMUL - UKRI-funded project on Moderation in Decentralised Social Networks (DSNmod) - 81,000 GBP 2022 - Now HKUST - GRF-funded project on ML methods for Congestion Control in SDN-based Networks - 600,000 HKD 2021 - Now KAUST - Competitive Research Grant on Machine Learning Architecture for Task-based Information Transfer - 400,000 USD. 2013-2017 Hong Kong PhD Fellowship (HKPFS) award, HK Research Grants Council - 155,000 USD for 4 years + tuition fees and travel grants. 2013-2017 HKPFS research travel grant award. 2017 Student Participation Grant, Local Computer Networks (IEEE LCN), IEEE CompSoc. 2015 Travel Grant award, Global Communications (GlobeCom) conference, IEEE ComSoc. 2007 FYP sponsorship award, Ministry of Telecommunications, Egypt. 2003-2007 Undergraduate Distinction award, for TGA of 85%-above, Assiut University. 2003-2007 Dean’s Honors, TGA of 85%+, Faculty of Computers and Information, Assiut University.GrantsStarting 2024: QMUL - Principal Investigator of UKRI-EPSRC funded New Investigator Award (NIA) project on Knowledge Delivery System for Machine Learning at Scale (KUber) - 650,000 GBP 2022 - Now: QMUL - CoI of UKRI-funded project on Moderation in Decentralised Social Networks (DSNmod) - 81,000 GBP 2022 - Now: HKUST - CoI of GRF-funded project on ML methods for Congestion Control in SDN-based Networks - 600,000 HKD 2021 - Now: KAUST - CoI of CRG- funded project on Machine Learning Architecture for Task-based Information Transfer - 400,000 USD