Title: Discrete System Identification Convergence Conditions and Error Bounds

Abstract: We consider data-driven methods for modeling discrete-valued dynamical systems evolving over networks. The spread of viruses and diseases, the propagation of ideas and misinformation, the fluctuation of stock prices, and correlations of financial risk between banking and economic institutions are all examples of such systems. In many of these systems, data may be widely available, but approaches to identify relevant mathematical models, including the underlying network topology, are not widely established or agreed upon. Classic system identification methods focus on identifying continuous-valued dynamical systems from data, where the main analysis of such approaches largely focuses on asymptotic properties, i.e., consistency. More recent identification approaches have focused on sample complexity, i.e., how much data is needed to achieve an acceptable model approximation. In this talk, we will discuss the problem of identifying a mathematical model from data for a discrete-valued, discrete-time dynamical system evolving over a network. Specifically, under maximum likelihood estimation approaches, we will provide both guaranteed sample complexity bounds and consistency conditions. Applications to the aforementioned examples will be discussed as time allows.

Biography: Carolyn received her PhD from Caltech, her MS from Carnegie Mellon, and her BS from California State Polytechnic University, all in Electrical Engineering. Prior to her PhD studies, she worked as a Research and Development Engineer for Hewlett-Packard in Silicon Valley. She is currently Associate Head and Professor at the University of Illinois at Urbana-Champaign in Industrial and Systems Engineering, and has held visiting positions at KTH (Stockholm, Sweden), Stanford University and Lund University (Sweden). She is currently the President-Elect for the IEEE Control Systems Society (CSS), and also serves on the Board of Governors for the CSS.  Carolyn is an IEEE Fellow, and has been the recipient of a National Science Foundation CAREER Award, an Office of Naval Research 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.