We develop tools for robustly recovering low-dimensional structure from non-ideal (corrupted, incomplete) high-dimensional observations, and apply them to solve problems in computer vision and signal processing. For example, we've recently worked on sparse vector and low-rank matrix recovery, their applications in automatic face recognition, tools for data clustering and classification.

For more examples and information, please see our publications page!