Aswin C Sankaranarayanan
Assistant Professor, ECE
Carnegie Mellon University
5000 Forbes Ave,
Pittsburgh, PA 15213
office. Porter B17
email. saswin -at- andrew .dot. cmu .dot. edu
Our fascination with detail is never-ending.
We have cameras that capture images with billions of pixels and videos
at millions of frames per second.
At the heart of such technologies is the simple idea that to represent a
signal with more detail, we need to sample it faster and with higher
resolution. Unfortunately, this idea does not extend to many applications
where sensing is inherently costly and where we cannot easily build high-resolution sampling-based sensors.
My research broadly focuses on the role of signal models in breaking
traditional sensing and processing limitations. My research focuses on two main topics:
compressive sensing and computational photography
non-linear signal models
A linear dimensionality reduction technique that is near-isometric (aka preserves pairwise distances) on a dataset.
Video compressive sensing using the CS-MUVI algorithm.
A greedy algorithm for recovering a matrix that is a sum of low rank and sparse matrices from its compressive measurements.
Video compressive sensing under a linear dynamical system model for the data. This encompasses data lying on an unknown low-dimensional subspace.
Fall 2013. Signals and Systems (18-290)
Spring 2013. Compressive Sensing and Sparse Optimization (18-799J)