Rate My Professor Nasim Ferdosian

Her research focuses on network automation, intelligent network control and management, radio resource management for 5G and beyond, wireless network optimisation, data-driven network control and management, and artificial intelligence and machine learning applications for next-generation mobile networks. She supervises higher degree by research students on topics such as low-power decentralized systems and cybersecurity anomaly detection. Ferdosian's publications have garnered over 250 citations on Google Scholar. Key works include 'Throughput-aware Resource Allocation for QoS Classes in LTE Networks' (Procedia Computer Science, 2015), 'Rated Window and Packet Size Differentiation Methods for Per-Rate Scheduling in LTE' (Arabian Journal for Science and Engineering, 2015), 'Two-Level QoS-Oriented Downlink Packet Schedulers in LTE Networks' (2013), '5G-VIOS: Towards next generation intelligent inter-domain network optimisation' (Computer Networks, 2024), 'Secured Real-Time Machine Communication Protocol' (Network, 2024), 'Low-Power Microcontroller-Based Decentralized Distributed Sequential Neural Network for Anomaly Detection in Wireless Sensor Networks' (2025), and 'A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection' (2025). In 2025, she was awarded a Trailblazer Early- and Mid-Career Researcher Grant.