معرفی
Alvaro Lopez Pellicer is a PhD student and Associate Lecturer at Lancaster University, affiliated with the departments of Politics, Philosophy and Religion, and Computing and Communications. His research focuses on cybersecurity, machine learning robustness, and autonomous systems, with a particular emphasis on adversarial threats and defense mechanisms. He contributes to research groups including the Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA), Security Lancaster (Secure Machine Learning and Intelligence), and LIRA’s Security and Defence sub-group.
His work spans topics such as federated learning for autonomous systems, deepfake detection, and multi-modal prototype-based models. He has published in conferences and journals on attack detection, noise reduction, and biometric identification using deep learning techniques.
Lopez Pellicer is actively involved in collaborative projects, such as the UNICAD framework for unified threat detection and the PUDD approach for robust deepfake identification. His research bridges theoretical advancements in machine learning with practical applications in autonomous systems and cybersecurity.
