I am a researcher with interests in computational electromagnetics, inverse problems, signal processing, and physics-informed machine learning. My work lies at the intersection of applied mathematics, physics, and engineering, where I develop computational methods for modelling and solving complex physical systems. My research has focused on electromagnetic modelling, microwave imaging and tomography, antenna design and characterization, inverse scattering, and biomedical signal processing. More recently, I have been investigating physics-informed neural networks and neural operators for solving forward and inverse problems governed by partial differential equations, particularly in EEG/MEG source modelling and medical imaging. Throughout my career, I have combined theoretical analysis with computational and experimental research, including numerical modelling, scientific computing, antenna measurements, vector network analyzer characterization, and anechoic chamber testing. I enjoy applying rigorous mathematical and physical principles to develop practical engineering solutions while also exploring innovative approaches to scientific education. My current research aims to integrate mathematical physics with artificial intelligence to build accurate, interpretable, and computationally efficient models for challenging problems in electromagnetics and biomedical engineering. I am particularly intereste