Data Driven Learning and Control seminar series is organized by the Information and Decision Science Lab at Cornell University and aims to explore the latest advancements and interdisciplinary approaches to data-driven learning and control systems.
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Discrete State System Identification: Examples and Error Bounds
Classic system identification methods focus on identifying continuous-valued dynamical systems from input-output data, where the main analysis of such approaches largely focuses on asymptotic convergence of the estimated models to the true models, i.e., consistency properties. More recent identification approaches have focused on sample complexity properties, i.e., how much data is needed to achieve an acceptable model approximation. In this talk I will give a brief overview of classical methods and then discuss more recent data-driven methods for modeling continuous-valued linear systems and discrete-valued dynamical systems evolving over networks. Examples of the latter include the spread of viruses and diseases over human contact networks, the propagation of ideas and misinformation over social networks, and the spread of financial default risk between banking and economic institutions. In many of these systems, data may be widely available, but approaches to identify relevant mathematical models, including underlying network topologies, are not widely established or agreed upon. We will discuss the problem of modeling discrete-valued, discrete-time dynamical systems evolving over networks, and outline analysis results under maximum likelihood identification approaches that guarantee consistency conditions and sample complexity bounds. Applications to the aforementioned examples will be further discussed as time allows.
Bio: Carolyn Beck received her Ph.D. from Caltech, her M.S. from Carnegie Mellon, and her B.S. from California State Polytechnic University, all in electrical engineering. Prior to her Ph.D. studies, she worked as a research and development engineer for Hewlett-Packard in Silicon Valley. She is currently a professor at the University of Illinois at Urbana-Champaign in the Department of Industrial and Systems Engineering, and has held visiting positions at KTH (Stockholm, Sweden), Stanford University and Lund University (Sweden). She serves as an associate editor for the IEEE Transactions on Control of Network Systems, on the IEEE Board of Governors for the Control Systems Society and is the President-Elect of CSS. Carolyn is an IEEE Fellow and was the recipient of a NSF CAREER Award, an ONR Young Investigator Award, and local teaching honors. Her research interests lie in the development of model approximation methods, network inference and aggregation, and distributed optimization and control, with applications to epidemic processes and energy networks.