Mobile network is evolving from a communication-only network towards one with joint
communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile …

Low-rank matrices play a fundamental role in modeling and computational methods for
signal processing and machine learning. In many applications where low-rank matrices …

High-dimensional probability offers insight into the behavior of random vectors, random
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …

The vast majority of today's critical infrastructure is supported by numerous feedback control
loops and an attack on these control loops can have disastrous consequences. This is a …

This paper describes two algorithms for state reconstruction from sensor measurements that
are corrupted with sparse, but otherwise arbitrary,“noise.” These results are motivated by the …

The availability of massive data and computing power allowing for effective data driven
neural approaches is having a major impact on machine learning and information retrieval …

In this paper, we develop a theory of matrix completion for the extreme case of noisy 1-bit
observations. Instead of observing a subset of the real-valued entries of a matrix M, we …

This paper treats the problem of minimizing a general continuously differentiable function
subject to sparsity constraints. We present and analyze several different optimality criteria …

Data collection is a crucial operation in wireless sensor networks. The design of data
collection schemes is challenging due to the limited energy supply and the hot spot problem …

This paper presents a framework for compressed sensing that bridges a gap between
existing theory and the current use of compressed sensing in many real-world applications …