Wednesday, 28 March 2018
9:00am - 9:45am
Campus Stuttgart-Vaihingen, Pfaffenwaldring 47
Room V 47.03
Surviving the Data Deluge: A Systems and Control Perspective
The past few years have witnessed a revolution in data collection capabilities: The development of low cost, ultra-low power sensors capable of harvesting energy from the environment has rendered ubiquitous sensing feasible. When coupled with a parallel growth in actuation capabilities, these developments open up the possibility of new technologies that can profoundly impact society, ranging from zero-emissions buildings to "smart" grids and managed aquifers to achieve long term sustainable use of scarce resources. A major road-block to realizing this vision stems from the curse of dimensionality. Successful operation in these scenarios requires the ability to timely extract relevant, actionable information from the very large data streams generated by the ubiquitous sensors. However, existing techniques are ill-equipped to deal with this "data avalanche".
This talk discusses the central role that systems theory can play in developing computationally tractable, scalable methods for extracting actionable information that is very sparsely encoded in high dimensional data streams. The key insight is the realization that actionable information can be often represented with a small number of invariants associated with an underlying dynamical system. Thus, in this context, the problem of actionable information extraction can be reformulated as identifying these invariants from (high dimensional) noisy data, and thought of as a generalization of sparse signal recovery problems to a dynamical systems framework. While in principle this approach leads to generically nonconvex, hard to solve problems, computationally tractable relaxations (and in some cases exact solutions) can be obtained by exploiting a combination of elements from convex analysis and semi-algebraic geometry. These ideas will be illustrated using examples from several application domains, including autonomous vehicles, computer vision, systems biology and economics. The talk will conclude by exploring the connection between hybrid systems identification, information extraction, and machine learning, and point out to new research directions in systems theory and in machine learning motivated by these problems.
Mario Sznaier is currently the Dennis Picard Chaired Professor at the Electrical and Computer Engineering Department, Northeastern University, Boston. 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 as chair of the IFAC Technical Committee on Robust Control. Past recent service include Program Chair of the 2017 IEEE Conf. on Decision and Control, General Chair of the 2016 IEEE Multi Systems Conference, Chair of the IEEE Control Systems Society Technical Committee on Computational Aspects of Control Systems Design (2013-2017), Executive Director of the IEEE CSS (2007-2011) and member of the Board of Governors of the CSS (2006-2014). In 2012 he received a distinguished member award from the IEEE Control Systems Society for his contributions to robust control, identification and dynamic vision. A list of publications and current research projects can be found at http://robustsystems.coe.neu.edu.