I design learning algorithms for reliable medical AI. My research focuses on uncertainty estimation, distribution shifts, and model robustness so that clinical decision-support systems can be trusted in the wild. I enjoy bridging rigorous methodology with deployable systems.
Medical AI Research
A novel method for uncertainty estimation in medical imaging AI systems, enabling more reliable predictions of cardiac function from echocardiograms. Published at MICCAI 2022, this work addresses the critical need for AI systems to quantify their own confidence in medical applications.
Mobile AI
Development of efficient deep learning models for cardiac function assessment on mobile devices, enabling point-of-care diagnostics in resource-limited settings.
MICCAI • 2022
IJCARS • 2022
SPIE Medical Imaging • 2022