Oct
29
Date: 29-October-2026
Time: 12:00 PM ET (New York Time)
Presenter: Dr. Alberto Natali
Based on the IEEE Xplore® article under the same title
Published IEEE Open Journal of Signal Processing, May 2022.
Download article: Original article is open access and publicly available for download. ARTICLE LINK
About this topic:
Graphs provide a powerful way to represent relationships between interacting entities, but in many real-world systems these relationships are not fixed. They can evolve as new observations become available, making it important to develop methods that can learn and track graph structures over time.
In this webinar, the presenter will revisit the problem of learning time-varying graphs from online data. Starting from the intuition behind graph learning and the challenges introduced by evolving network structures, the presentation will introduce a general framework for adapting graph estimates as new data arrives. The approach is designed to be model-independent, making it applicable to several common graph-based models and allowing different assumptions about the relationship between the data and the underlying graph to be incorporated into the learning process.
The presentation will focus on the main ideas and intuition behind the proposed methodology, rather than on technical details alone. He will also discuss the experimental results and illustrate how online graph learning can be used to capture changing relationships in networked data. The goal is to provide an accessible overview of the framework and its broader relevance to signal processing and machine learning on graphs.
About the presenter:
Dr. Alberto Natali
Alberto Natali (M’19–M’24) received the B.Sc. degree in computer engineering and the M.Sc. degree in information and robotics engineering, both from the University of Perugia, Perugia, Italy, and the Ph.D. degree in electrical engineering from Delft University of Technology, Delft, The Netherlands, under the supervision of Prof. Geert Leus in 2016, 2019 and 2024 respectively. His doctoral research focused on signal processing and optimization on graphs, with particular emphasis on learning time-varying structures and generalizing convolution principles.
He was a Ph.D. researcher at Delft University of Technology from 2019 to 2024. In 2023, he was a Machine Learning Intern at Qualcomm, Cork, Ireland. Since 2024, he has been a Machine Learning Scientist at Booking.com, Amsterdam, The Netherlands, working in the Security, Safety, and Fraud organization. His research interests include algebra, optimization, and machine learning on graphs.
Dr. Natali was a member of the Institute of Electrical and Electronics Engineers from 2019 to 2024.
