SPS-DSI (DEGAS) Webinar: Task-driven Topology Inference for Signal processing and Learning over Topological Domains
Date: 5 March 2025
Time: 9:00 AM ET (New York Time)
Presenter(s): Dr. Paolo Di Lorenzo
The DEGAS Webinars will be offered to multiple time zones here.
DEGAS Webinar Series is an event initiated by the Data Science Initiative (DSI) of the IEEE Signal Processing (SP) Society. The goal is to provide the SP community with updates and advances in learning and inference on graphs. Signal processing and machine learning often deal with data living in regular domains such as space and time. This webinar series will cover the extension of these methods to network data, including topics such as graph filtering, graph sampling, spectral analysis of network data, graph topology identification, geometric deep learning, and so on. Applications can for instance be found in image processing, social networks, epidemics, wireless communications, brain science, recommender systems, and sensor networks. These bi-weekly webinars will be hosted on Zoom, with recordings made available in the IEEE Signal Processing Society’s YouTube channel following the live events. Further details about live and streaming access will follow. Each webinar speaker will give a lecture, which is followed by Q&A and discussions.
Abstract
Processing data defined over irregular domains is crucial across various applications including biological, technological, and social networks. While graphs effectively model pairwise interactions, they fail to capture higher-order dependencies. To address this limitation, topological structures such as simplicial and cell complexes enable advanced topological signal processing (TSP). Crucially, the choice of the topological space directly impacts our ability to perform effective signal processing, information extraction, and adaptive learning. However, in many cases, this topological knowledge is incomplete or unavailable, necessitating the development of latent topology inference methods tailored to specific learning tasks. In this talk, hinging on fundamental TSP tools, I will present several task-driven approaches for latent topology inference. These methods optimize higher-order topology to improve the performance of key TSP applications, including sparse signal representation, dictionary learning, adaptive filtering, and topological deep learning. Finally, I will discuss recent advances in processing signals over cellular sheaves—structures that associate vector spaces with elements of a topological domain and encode relationships through restriction maps. In this context, I will highlight a principled algorithm that jointly learns topology and restriction maps along edges to minimize total variation over the cellular sheaf.
Biography
Paolo di Lorenzo received the M.Sc. and Ph.D. degrees in telecommunication engineering from Sapienza University of Rome, Rome, Italy, in 2008 and 2012, respectively.
He is an Associate Professor with the Department of Information Engineering, Electronics, and Telecommunications at Sapienza University of Rome, Rome, Italy. He is also a member of ELLIS, the European Laboratory for Learning and Intelligent Systemis the technical manager of the SNS-JU European Project 6G-GOALS, and was the Principal Investigator of CNIT-Sapienza Research Unit in the H2020 European Project RISE 6G. His research interests include topological signal processing, goal-oriented and semantic communications, distributed optimization, and federated learning. He held a visiting research appointment with the Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA, USA.s. Wearing yet another hat, he serves as the co-director of the Applied Algebraic Topology Research Network.
Dr. Di Lorenzo is the recipient of the 2022 EURASIP Early Career Award, and of three best student paper awards at IEEE SPAWC10, EURASIP EUSIPCO11, and IEEE CAMSAP11, respectively. He is also the recipient of the 2012 GTTI (Italian National Group on Telecommunications and Information Theory) Award for the Best Ph.D. Thesis in communication engineering. He served as an Associate Editor for the IEEE Transactions on Signal Processing, for the IEEE Transactions on Signal and Information Processing over Networks, and for the EURASIP Journal on Advances in Signal Processing. He is currently a Senior Area Editor of the IEEE Transactions on Signal Processing.
