We study the problem of learning a linear system model from the observations of M clients.
The catch: Each client is observing data from a different dynamical system. This work …

Stabilizing an unknown dynamical system is one of the central problems in control theory. In
this paper, we study the sample complexity of the learn-to-stabilize problem in Linear Time …

Engineered cyberphysical systems are growing increasingly large and complex. These
systems require scalable controllers that robustly satisfy state and input constraints in the …

This paper presents two novel algorithms for approximately projecting symmetric matrices
onto the Positive Semidefinite (PSD) cone using Randomized Numerical Linear Algebra …

Random projection can reduce the dimension of data while capturing its structure and is a
fundamental tool for machine learning, signal processing, and information retrieval, which …

This work considers the problem of learning the Markov parameters of a linear system from
observed data. Recent non-asymptotic system identification results have characterized the …

In this article, a novel fast randomized subspace system identification method for estimating
combined deterministic-stochastic LTI state-space models, is proposed. The algorithm is …

Identifying a high-fidelity model of nonlinear dynamic systems is a prerequisite for achieving
desired specifications in any model-based control design technique. This is because, most …