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Ricardo Gonzales

DPhil, FSCMR

Clarendon Scholar

Artificial Intelligence in Cardiovascular Imaging

My research focus is on developing robust deep learning approaches for accountable contrast-agent-free cardiac magnetic resonance (CMR) imaging in clinical applications. I design novel data-driven methods to automatically derive predictive biomarkers. My DPhil programme is funded by the Clarendon Fund Scholarship and Radcliffe Department of Medicine Scholar Programme.

Previously, I received my undergraduate degree in Electrical Engineering at UTEC (Peru) and my research training at Yale University (USA) and Lund University (Sweden), where I developed tools for the assessment of diastolic function in CMR, and its relationship to atrial remodeling. Outside of work, I serve as the Computer Science Head at REPU, a career progression program.