Bio

Since 2024, I am a Ramon y Cajal tenure-track researcher at the department of Engineering in Universitat Pompeu Fabra. My research focuses in medical image analysis, and is at the intersection of applied mathematics, machine learning and image processing. I develop novel computational techniques for better assessment of the cardiovascular system, assessed through ultrasound images. My methodological research tackles how to combine ML, computational models of the circulation and a priori knowledge from physiology, to be able to identify correct patterns in noisy and biased datasets.

I did a dual undergraduate training in both Mathematics (Bachelor and Master of Sciences) and  Computer Science (Engineer’s Degree) in Universitat Politecnica de Catalunya (Barcelona, Spain) and University of Bonn (Germany) . I defended my PhD in 2019 (Cum Laude), funded by the Marie Sklodowska Curie project “Cardiofunxion”, a collaboration between Philips Research (Paris, supervised by Dr. De Craene), a major manufacturer of medical imaging devices, and the academic research group Physense on cardiac image analysis at Universitat Pompeu Fabra (UPF, Barcelona, supervised by Prof. Gonzalez Ballester and Prof. Bijnens. From 2020 to 2022, I did a postdoc in CREATIS (Lyon, France), one of the largest research laboratories working on medical imaging, where I worked on the use of machine learning to discover hierarchical relationships between different cardiovascular image modalities. In late 2022, I was a Margarita Salas fellow in BCNatal (Barcelona, Spain, a leading clinical research center in fetal medicine) and UPF, where I studied how the fetal circulation is altered under adverse conditions.

 I am a co-Principal Investigator in 2 projects: CoLLAGE (PID2023-149959OA-I00, funded by the Spanish Research Agency), in collaboration with the Universidade de Santiago de Compostela (USC, Santiago, Spain), where we develop new computational techniques for the longitudinal analysis of fetal growth trajectories to better identify pathologies, and another one "Machine Learning-based phenogroup of preeclampsia for a new classification and personalized prevention of the posterior cardiovascular risk"  Marato de TV3 (2024153031), in collaboration with the BCNatal research group on fetal medicine.

My pre- and post-doctoral experience has helped me to consolidate technical expertise in machine learning, especially in its applications to medical image analysis, and a deep knowledge of cardiac physiology and its biophysical modeling. I am author or coauthor of  ~15 peer-reviewed journal papers and ~10 conference proceeding (~8-10 pages). 3 extra papers are currently under review (1 as main author). I demonstrated supervisory skills experience supervising research thesis of undergraduate students (10). I am currently co-supervising 6 PhD in different institutions:  2 in biomedical engineering at UPF in USC, and 2 in obstetrics (BCNatal, PhD awarded by the Universitat de Barcelona). I reviewed ~30 articles for international methodological journals in medical image and conferences. From 2022 to 2024 I was an elected nucleus member of the working group on e-Cardiology of the European Society of Cardiology, where we worked towards the deployment of computational tools in clinical cardiology. My national and international collaborators include the different stakeholders involved in medical imaging: academia (USC, UPF and CREATIS), industry (Philips) and reference hospitals (CHU Caen, Hospital Clinic and BCNatal in Barcelona, Sickkids in Toronto).