Decision and Control Lecture Series
Decision and Control Laboratory, Coordinated Science Laboratory
Surviving the Upcoming Data Deluge: A Systems and Control Perspective
Dennis Picard Chaired Professor
Electrical and Computer Engineering Department
Northeastern University, Boston
Wednesday, October 23, 2013
3:00 p.m. to 4:00 p.m.
B02 CSL Auditorium
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 control applications 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. To successfully operate in these scenarios, controllers will need 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. We will conclude the talk by exploring the connection between hybrid systems identification, information extraction, and machine learning, and point out to new research directions in systems theory 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 a member of the Board of Governors of the IEEE Control Systems Society. Additional recent service includes CSS Executive Director (2007-2011), Program Chair of the 2009 IFAC Symposium on Robust Control Design, and Program vice-chair of the 2008 IEEE Conf. on Decision and Control. Dr. Sznaier was a plenary speaker at the 2012 IFAC Symposium on Robust Control Design, 2012 IFAC Symposium on System Identification and the 2012 Mediterranean Control Conference, and delivered a Semi-Plenary lecture at the 2012 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. A list of publications and current research projects can be found at http://robustsystems.coe.neu.edu.
PLEASE JOIN US FOR COOKIES AND COFFEE AT 2:30PM BEFORE THE SEMINAR IN ROOM 154 COORDINATED SCIENCE LABORATORY