UTRC CDS Invited Lecture: Mario Sznaier, "Interplay between Big Data and Sparsity in System Theory"
Thursday, October 5, 2017
1146 AV Williams Building
United Technologies Research Center
Control and Dynamical Systems Invited Lecture
The Interplay between Big Data and Sparsity in System Theory
Dennis Picard Trustee Professor
Department of Electrical and Computer Engineering
Arguably, one of the hardest challenges faced now by the systems community stems from the exponential explosion in the availability of data, fueled by recent advances in sensing and actuation capabilities. Simply stated, classical techniques are ill equipped to handle very large volumes of (heterogeneous) data, due to poor scaling properties and to impose the structural constraints required to implement ubiquitous sensing and control.
The goal of this talk is to explore how this “curse of dimensionality” can be potentially overcome by exploiting the twin “blessings” of self-similarity (high degree of spatio-temporal correlation in the data) and inherent underlying sparsity. While these ideas have already been recently used in machine learning (for instance in the context of dimensionality reduction and variable selection), they have hitherto not been fully exploited in systems theory. By appealing to a deep connection to semi-algebraic optimization, rank minimization and matrix completion we will show that, in the context of systems theory, the limiting factor is given by the ``memory" of the system rather than the size of the data itself, and discuss the implications of this fact. These concepts will be illustrated examining examples of "easy" and "hard" problems, including the synthesis of filters and controllers subject to information flow constraints, and identification of classes of non-linear systems.
The talk will conclude with an application of these ideas to the non-trivial problem of extracting actionable information from very large data streams. In particular, we will show how exploiting sparsity leads to tractable, scalable solutions to the problems of anomaly detection and activity analysis from video streams
Mario Sznaier is the Dennis Picard Chaired Professor at the Electrical and Computer Engineering Department, Northeastern University. Prior to joining Northeastern University, Dr. Sznaier was a Professor of Electrical Engineering at the Pennsylvania State University and also held visiting positions at the California Institute of Technology. His research interest include robust identification and control of hybrid systems, robust optimization, and dynamical vision. Dr. Sznaier is currently serving as an associate editor for the journal Automatica and Program Chair of the 2017 IEEE Conference on Decision and Control. In 2012 he received a distinguished member award from the IEEE Control Systems Society for his contributions to robust control, identification and dynamic vision.