Oscar Leong's research interests lie at the intersection of inverse problems, signal processing, and machine learning. His work incorporates data-driven priors for natural signals, such as deep generative models, to help solve ill-posed inverse problems and uses tools from high-dimensional probability, random matrix theory, and optimization to develop statistical and algorithmic guarantees for such approaches.