Dr Poonam Yadav
SYSTRON Lead
Current research is on making the Internet of Things (IoT) and edge computing systems resilient, reliable and robust.
Current research is on making the Internet of Things (IoT) and edge computing systems resilient, reliable and robust.
Current research on data protection and the GDPR, ontologies, and blockchain technology.
Researching on enhancing resilience in software-defined Internet of Things networks against link failure.
Current research on data sharing in Autonomous vehicles through blockchain technologies.
Dr Rana Muhammad Sohaib is a Research Associate within the SYSTRON Lab working on the CHEDDAR project .His research interests include energy efficiency, Open RAN, radio resource management, vehicular communication systems, and LTE, 5G, and 6G technologies.
Supporting research in IoT systems and joining the EPSRC CHEDDAR project.
Dr Panagiotis (Panos) Papanastasiou is a Research Associate within the SYSTRON Lab. His interest is Quantum Cryptography and specifically how we can enhance Network security by using hybrid settings of PQC and QKD in wireless communications.
Supporting the integration of RAN Intelligent Controllers (RICs) and xApps as well as building a testbed with srsRAN and USRPs
Dr Kavan Fatehi is a Research Associate within the SYSTRON Lab working on the CHEDDAR project .His interest is self-supervised learning, representation learning and neuro-symbolic AI.
Dr Kangfeng (Randall) Ye is a Research Associate within the SYSTRON Lab, working on the EPSRC CHEDDAR project. His previous work has involved formal verification and proof.
Current research is on networking protocols and routing, and Internet architectures and governance.
Supporting research into 6G networks and working in the EPSRC funded CHEDDAR project.
Researching on embedded systems, FPGA Design, high-speed digital design, ultra low-power sensor systems.
Ahsan Raza Khan is a Research Associate at the SYSTRON lab, which is funded by the EPSRC CHEDDAR project. His research interests include federated learning, vision-assisted wireless networks, digital twins for wireless networks, and privacy-aware machine learning.