Summary
Gabriel Bernardino is a Ramon y Cajal tenure-track fellow and computer scientist with a decade of experience developing novel machine learning methods for cardiovascular imaging, especially echocardiography and cMRI. He builds robust, interpretable algorithms that detect subtle deviations from normality, combining classical, reinforcement and deep learning with CFD, mesh and image-processing techniques. His applied research spans hospitals, industry (Philips Research) and academia, informing clinically-relevant tools for rapid cardiac diagnosis. Fluent in Python, C++ and JavaScript, he pairs strong mathematical training with visualization and ML stacks (VTK, Qt, ITK, TensorFlow, PyTorch, GPflow) to move models toward clinical use. An unusual strength is his track record of translating computational fluid dynamics and mesh expertise into image-based phenotyping problems, enabling richer physics-informed analyses of cardiac function. Based in Barcelona, he directs research that balances methodological rigor with real-world hospital deployment.
10 years of coding experience
6 years of employment as a software developer
Master's Degree, Mathematics, Master's Degree, Mathematics at Rheinische Friedrich-Wilhelms-Universität Bonn
Engineer's degree, Engineer's degree at CFIS-UPC
Doctor of Philosophy (Ph.D.), Biomedical/Medical Engineering, Cum laude, Doctor of Philosophy (Ph.D.), Biomedical/Medical Engineering, Cum laude at Universitat Pompeu Fabra
UPC Universitat Politècnica de Catalunya
Spanish, Catalan, German, English, French