Scalable Adaptive Yet Efficient Distributed (SAYED) Systems Group performs systems-focused research by exploring existing problems of mainstream Networked and Distributed Systems and finding solutions by designing and prototyping scalable adaptive yet efficient systems of the future. We are broadly interested in Systems for ML, ML for Systems, Distributed and Federated Learning, Computer Networks, Fog/Cloud Computing, SDN and Programmable Networks.
The group is part of School of Electronic Engineering and Computer Science, Queen Mary University of London, UK. The group is part of and has close ties and ongoing collaborations with the members of networks group at QMUL as well as many research groups world-wide such as Edinburgh, UK; Imperial College London, UK; KAUST, Saudi Arabia; HKUST, Hong Kong; UCD, Ireland; KTH, Sweden. The group aims to publish their research work in top-tier venues such as MLSys, EuroSys, NeurIPS, ICML, MobiCom, MobiSys, INFOCOM, ICDCS, AAAI and journals such as IEEE/ACM Transaction on Networking (ToN), Mobile Communication (TMC), Neural Networks and Learning Representations (TNNLS), Internet of Things Journal (IoTJ). The group head, Dr. Ahmed, has a track-record of supervising research projects of Intern/MSc/PhD students and securing grant funding from UKRI EPSRC, KAUST CRG, and HKUST GRF. The group has connections with various industrial partners such as Nokia Bell Labs, Samsung AI, and IBM Research, Huawei Research who support the group with guidance, advice, and other forms of in-kind contributions. Group members are also invited to join our industrial partners' Group for internship to get hands-on practical exposure.