Ben-Gurion University of the Negev, Israel
"Statistical Graph Signal Processing with Applications to Smart Grids"
Graphs are fundamental mathematical structures that are widely used in various fields for network data analysis to model complex relationships within and between data, signals, and processes. In particular, graph signals arise in many modern applications, leading to the emergence of the area of graph signal processing (GSP) in the last decade. GSP theory extends concepts and techniques from traditional digital signal processing (DSP) to data indexed by generic graphs, including the graph Fourier transform (GFT), graph filter design, and sampling and recovery of graph signals. However, most of the research effort in this field has been devoted to the purely deterministic setting. In this study, we consider the extension of statistical signal processing (SSP) theory by developing graph SSP (GSSP) methods and bounds. Special attention will be given to the development of GSP methods for monitoring the power systems, which has significant practical importance, in addition to its contribution to the enrichment of theoretical GSSP tools. In particular, we will discuss the following problems (as time permits): 1) Bayesian estimation of graph signals in non-linear models; 2) the identification of edge disconnections in networks based on graph filter representation; 3) the development of performance bounds, such as the well-known Cramér-Rao bound (CRB), on the performance in various estimation problems that are related to the graph structure; 4) the detection of false data injected (FDI) attacks on the power systems by GSP tools; 5) Laplacian learning with applications to admittance matrix estimation. The methods developed in these works use GSP concepts, such as graph spectrum, GSP, graph filters, and sampling over graphs.
Tirza Routtenberg is an Associate Professor in the School of Electrical and Computer Engineering at Ben-Gurion University of the Negev, Israel. In addition, she is a William R. Kenan, Jr., Visiting Professor for Distinguished Teaching at the Electrical and Computer Engineering Department at Princeton University for 2022-2023. She was the recipient of four Best Student Paper Awards at international conferences. She is currently an Associate Editor of IEEE Transactions on Signal and Information Processing Over Networks and of IEEE Signal Processing Letters. In addition, she is part of the SPS Technical Directions Board Representative on the Education Board. Her research interests include statistical signal processing, graph signal processing, and optimization and signal processing for smart grids.