Team Member

Trained in Biomedical Engineering (UC3M & Georgia Tech, 2020), I completed a BSc thesis on cardiac signal processing (UPV), an MEng in Computational Biology & Bioinformatics at UC Berkeley (Fung Institute Fellow, ‘la Caixa’ Fellow; capstone in deep learning for proteomics), and internships at Stanford in computational arrhythmia analysis. I founded the startup SWiiFT in Silicon Valley, then returned to Stanford as a researcher before moving to ETH Zurich for an MSc in Bioelectronics (Rafael del Pino Excellence Fellow), including work at the Institute of Neuroinformatics on flexible electrodes and time-series clustering. My master’s thesis on generative AI denoising of cardiac time series led to my current PhD in the Medical Data Science group and the AI Center at ETH Zurich

I am a member of the European Society of Cardiology, the European Heart Rhythm Association, and the American Heart Rhythm Association through Stanford University, as well as of the IEEE through ETH Zurich. My interests lie at the intersection of machine learning, signal theory, and medical applications, particularly in cardiology. I am keen on exploring multimodal learning to efficiently model inference and generative pathways.