I hold a Bachelor's degree in Biomedical Engineering, a Master's degree in Medical Image Analysis, and a PhD focused on AI for medical image analysis. Throughout my academic and professional career, I have worked on a range of healthcare AI applications.

I completed my Bachelor's thesis and an internship at SIBELMED, where I developed signal processing algorithms for the quality validation of physiological signals. During my Master's, I specialized in medical image analysis, and my PhD research focused on developing deep learning methods for medical imaging, with an emphasis on improving the efficiency, and focus of AI models in the context of stroke imaging.

My research interests lie at the intersection of artificial intelligence and healthcare, with a particular focus on developing trustworthy, clinically useful AI systems that assist healthcare professionals in improving patient care. I am interested in medical image analysis, physiological signal processing, computer vision, and machine learning methods that can be translated into real-world clinical practice.

Alongside my research, I enjoy teaching and mentoring students. I currently teach Artificial Intelligence, Natural Language Processing, and Computer Vision at both bachelor's and master's levels at EADA Business School.

My long-term goal is to contribute to the development of AI technologies that are not only technically advanced but also reliable, interpretable, and capable of making a meaningful impact on clinical practice.