My research focuses on using convex and non-convex optimization methods to model and solve problems in signal and information processing, physical sciences, and machine learning. I am especially interested in designing optimization procedures that come with theoretical performance guarantees and that are scalable to large data sets. A common theme of my work is leveraging prior structures and domain knowledge in a computationally effective way; these structures could be sparsity, manifold, smoothness, dynamics, graphs, and so on. One of the most interesting parts of this work is to explore the trade-offs between computational time, statistical performance, and sampling complexity. A few current projects include tensor decomposition and completion in machine learning, super-resolution and phase retrieval in imaging, spectral estimation in sensor array and radar problems, and causality inference in graphical modeling.

News 2017