BIOGRAPHY
BROAD RESEARCH INTERESTS
Wavelets, Sparse Optimization Theory, Inverse Problems, and Data-driven learning methods
RESEARCH FOCUS/EXPERIENCE
Sparse optimization theory (popularly known as Compressive Sensing) is an interface area between Algebra and Optimization, which aims at providing some classes of linear systems with sparse (or economical) descriptions. Applications of this research area are far and wide in diverse fields including medical imaging.
My current research interests lie in Frame theory, Sparsity-driven optimization techniques, and their applications involving Data-driven learning and Inverse problems. My previous research, nevertheless, was directed towards `Wavelets and their applications in image analysis.’