DF seminar: The ‘Power’ of Graph Signal Processing
We are happy to present Raksha Ramakrishna – A postdoctoral Researcher at KTH Royal Institute of Technology. Raksha Ramakrishna will talk about The ‘Power’ of Graph Signal Processing.
Date and time: 11 February, 12 pm – 1 pm
Speaker: Raksha Ramakrishna
Title: The ‘Power’ of Graph Signal Processing
Meeting ID: 674 3268 2790
Watch the recorded presentation:
Abstract: The theory of graph signal processing (GSP) was formulated to extend fundamental insights that come from the frequency analysis for time series to the domain of signals indexed by graphs. The power grid is one of the foremost examples of a large-scale man-made network. It is therefore natural to see measurements from the power grid as graph signals and model power grid measurements using tools from GSP. In this talk, the paradigm of GSP will be discussed in the context of the power grid where a spatio-temporal generative model for voltage measurements is developed from first principles by leveraging system-level knowledge of power systems. How GSP based tools can be effectively used for different purposes in the power grid is demonstrated using both synthetic data and real-world measurements. Ultimately, the GSP based modelling strategy for the power grid illustrates how a variety of data such as those coming from financial or social networks can be analyzed using similar tools presented in this talk.
Bio: Raksha Ramakrishna received her M.S. and PhD degrees in Electrical Engineering from Arizona State University (ASU) in 2017 and 2020 respectively and her B.E. degree in Electronics and Communications Engineering from Rashtreeya Vidyalaya College of Engineering, Bangalore, India in 2014. She is currently a Postdoctoral Researcher at the Division of Network and System Technology with Prof. György Dán’s group and also affiliated to the Center for Trustworthy Edge Computing Systems and Applications (TECoSA). Her research interests are in the domains of statistical signal processing, smart grids and more recently in security and privacy in federated machine learning systems.