My research is primarily focused on development of advanced MRI methods for rapid, motion-robust, and quantitative imaging. During the past years, our team has developed a number of novel MRI techniques that combine advanced data acquisition and image reconstruction approaches for imaging different organs in the human body. In particular, I have been the leading driver for the development, optimization, and extension of a rapid MRI technique called GRASP (Golden-angle RAdial Sparse Parallel imaging), which combines compressed sensing, parallel imaging, and golden-angle radial sampling for fast free-breathing dynamic MRI. At NYU Langone, I lead a research lab that has been funded by the National Institutes of Health, and ongoing research projects in my lab include developments of (1) novel rapid MRI methods based on advanced compressed sensing, low-rank, and deep learning models, (2) novel data acquisition schemes and motion-compensation strategies, and (3) novel quantitative MRI methods that can provide more accurate image biomarkers for disease diagnosis. My overarching goal is to synergistically combine methods developed in these three aspects to deliver advanced imaging frameworks that can ultimately increase the utilization and efficacy of MRI for broader clinical use. This entails more than just acceleration of existing imaging protocols, and it could lead to a shift of the day-to-day clinical workflow from conventional time-consuming, inefficient, and tailored acquisitions of carefully targeted slabs towards rapid continuous and comprehensive acquisitions, with user-defined reconstructions that can be adapted retrospectively to answer different clinical questions.