We study a deep learning inspired formulation for the blind demodulation problem, which is
the task of recovering two unknown vectors from their entrywise multiplication. We consider …

We consider the task of recovering a pair of vectors from a set of rank one bilinear
measurements, possibly corrupted by noise. Most notably, the problem of robust blind …

The problem of estimating a sparse signal from low dimensional noisy observations arises in
many applications, including super resolution, signal deconvolution, and radar imaging. In …

We consider the bilinear inverse problem of recovering two vectors, x∈ RL and w∈ RL,
from their entrywise product. We consider the case where x and w have known signs and …

We consider the task of recovering two real or complex $ m $-vectors from phaseless Fourier
measurements of their circular convolution. Our method is a novel convex relaxation that is …

Optical wireless communication becomes a key enabling technology for achieving ultra-high
data rate requirements in beyond 5G systems. In this paper, to reduce both channel …

This thesis is concerned with the design and analysis of computationally efficient algorithms
for large-scale optimization and scientific computing. It aims to address two primary …

Continuous optimization has become a prevalent tool across the sciences and engineering.
Modern applications have displayed steady growth in problem sizes. Such sizes often …

By exploiting and leveraging the intrinsic properties of the observed signal, many signal
processing and machine learning problems can be effectively solved by transforming them …

Recent advances in signal processing, machine learning and deep learning with sparse
intrinsic structure of data have paved the path for solving inverse problems in acoustics and …