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 on clinical outcomes. My research is funded by 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.