Recent advances in computational models of signal propagation and routing in the human
brain have underscored the critical role of white-matter structure. A complementary …

In this paper, we focus on applications in machine learning, optimization, and control that
call for the resilient selection of a few elements, eg features, sensors, or leaders, against a …

We propose methods to solve time-varying, sensor and actuator (SaA) selection problems
for uncertain cyber-physical systems. We show that many SaA selection problems for …

This paper studies the benefits of time-varying actuator scheduling to the controllability of
complex networks. The network dynamics are described by a single-input discrete-time …

We study the problem of jointly designing a sparse sensor and actuator schedule for linear
dynamical systems while guaranteeing a control/estimation performance that approximates …

We consider the time-varying actuator placement in continuous time, where the goal is to
maximize the trace of the controllability Grammian. A natural relaxation of the problem is to …

This paper is concerned with develo** an adaptive controller for Partially-Observed
Boolean Dynamical Systems (POBDS). Assuming that partial knowledge about the system …

A key problem in emerging complex cyber-physical networks is the design of information
and control topologies, including sensor and actuator selection and communication network …

This paper is concerned with optimal estimation of the state of a Boolean dynamical systems
observed through correlated noisy Boolean measurements. The optimal Minimum Mean …

We investigate a novel approach to resilient distributed optimization with quadratic costs in a
multiagent system prone to unexpected events that make some agents misbehave. In …