My research interests primarily include areas at the intersection of inverse problems, statistical signal processing, machine learning, and optimization. More recently, I have been working on the applications of deep learning for solving inverse problems that frequently arise in medical imaging applications such as computed tomography, magnetic resonance imaging, etc. I take interest in designing novel deep neural network architectures and learning strategies that are particularly suited for image reconstruction problems. Although I primarily focus on medical imaging applications, the learning paradigms I develop often transcend the application at hand and apply to more general computer vision tasks. I am also keenly interested in developing robust and scalable optimization algorithms with theoretical guarantees for high-dimensional signal estimation problems with structural constraints such as sparsity and low-rank.