My research sits at the boundaries of several disciplines, including mathematics, computer science, and statistics. I have worked on or am working in three main areas: (i) computational harmonic analysis or sparse approximation and sparse signal recovery, (ii) algorithms (especially sublinear or streaming algorithms), and (iii) applications of sparse analysis in signal processing, sensor networks, network traffic analysis, inverse problems, and high throughput biological screens. This interplay of mathematics and computing is crucial in identifying and solving fundamental problems in science and my vision for my research is to make lasting contributions to both mathematics and science through this interdisciplinary work.
If it ain’t broke, don’t fix it.
A mathematician’s take on machine learning.
AAFFT on an Arduino: Smart data collection and processing
(Sparse) Feature selection for sparsely sampled data, with applications to single-cell RNA seq data