Yuzhe Yang is a PhD candidate working at the intersection of machine learning (ML), artificial intelligence (AI), and digital health. He is building innovative neural learning pipelines that combine MATLAB’s capacity to manipulate signals and matrices with contemporary deep learning approaches with the goal of developing new modalities and applications for digital health. His recent work includes a project that tackled the problem of imbalance in real-world data and yielded new tools and insights for self-supervision and feature-level smoothing in deep learning. In another project, he developed a novel approach—now in use—for contactless, in-home monitoring of Covid-19. As a MathWorks Fellow, he has developed (and will soon begin testing) the first ML-based solution that uses a person’s nocturnal breathing signals to detect Parkinson’s disease, estimate disease severity, and track disease progression. Making innovative use of MATLAB to address core challenges in deep learning, Yuzhe is making valuable contributions to smart health sensing solutions and advancing the field of digital health.