I hold an Engineer's Degree in Computer Science and a Bachelor in Mathematics by the Universitat Politècnica de Catalunya, and a Msc in Mathematics by the University of Bonn. I did a PhD in Information and Communication Technologies (Universitat Pompeu Fabra, Barcelona, Spain, 2019), carried out in collaboration with Philips Research (Paris, France) within the Marie Swodlaska Curie European Industrial Doctorate Cardiofunxion. Afterwards, I was a postdoctoral researcher in CREATIS (Lyon, France), where I investigated reinforcement learning strategies for cost-effectively combining different imaging modalities for medical diagnosis. Now I am a Margarita Salas fellow at the Physense group at UPF.

My research objective is to improve current assessment of cardiovascular images. While machine learning has shown great potential in computer vision, its applications to medical images are still challenging, given data scarcity and the amount of noise present. My aim is to develop machine learning techniques that not only learn from data, but also incorporate physiological knowledge, thus being more robust and interpretable. My research focuses on deriving interpretable biomarkers of pathologies from populations, useful not only for diagnosis purposes but also to understand the underlying pathophysiology.  Currently, my main clinical interest lies in fetal cardiology: identifying how cardiovascular abnormalities cause a gestational impairment in fetal ultrasound images (Doppler and B-mode).