Phase retrieval consists in the recovery of a complex-valued signal from intensity-only
measurements. As it pervades a broad variety of applications, many researchers have …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

Holography provides access to the optical phase. The emerging compressive phase
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …

We consider the problem of signal estimation in generalized linear models defined via
rotationally invariant design matrices. Since these matrices can have an arbitrary spectral …

As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

We consider the problem of estimating a signal from measurements obtained via a
generalized linear model. We focus on estimators based on approximate message passing …

We consider solving ill-posed imaging inverse problems without access to an explicit image
prior or ground-truth examples. An overarching challenge in inverse problems is that there …

We propose a novel approach for lensless single-shot phase retrieval, which provides pixel
super-resolution phase imaging. The approach is based on a computational separation of …

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes.
We identify the connection between the difficulty level and the number and variety of …