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James Pope is a Senior Lecturer at the University of Bristol, where he specializes in machine learning techniques for resource-constrained devices. His research incorporates traditional methodologies such as decision trees, naive Bayes, and logistic regression, as well as emerging technologies like graph neural networks. Notable areas of his work include anomaly detection in edge devices for cybersecurity, pervasive health monitoring through wearables, and the utilization of smart home sensor devices. Pope is actively involved in several research projects, including the TORUS project which examines human activity recognition in Parkinson’s disease using multimodal data. Additionally, he co-investigates the ELABORATOR project, focusing on innovations for safe and sustainable urban mobility, and the CHARIOT project aimed at addressing the impacts of ransomware on Industrial IoT systems. His early research efforts were significant in developing efficient routing strategies for IoT networks. Pope is affiliated with the Intelligent Systems Laboratory and the Communication Systems Networks group, highlighting his commitment to advancing technology in smart city applications and healthcare.
University of Bristol • Bristol, UK
Senior Lecturer in the Department of Computer Science specializing in machine learning for resource-constrained devices.
Department of Physics research themes include Astrophysics, Materials and Devices, Particle Physics, and Quantum and Soft Matter.