SPS-ASI Webinar: Model-Based Deep Learning in Signal Processing and Communications
Date: 10 September 2024
Time: 2:00 PM (CEST)
Presenter(s): Dr. Nir Shlezinger
The ASI Webinar Series is an event initiated by the Autonomous System Initiative (ASI) of the IEEE Signal Processing (SP) Society. The goal is to offer the SP community with free webinars looking into the future of autonomous systems. These monthly webinars are hosted on Zoom platform.
Abstract
Signal processing and communications have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information, and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. On the other hand, purely data-driven approaches that are model-agnostic are becoming increasingly popular as datasets become abundant, and the power of modern deep learning pipelines increases. Deep neural networks (DNNs) use generic architectures that learn to operate from data and demonstrate excellent performance, especially for supervised problems. However, DNNs typically require massive amounts of data and immense computational resources, limiting their applicability for some scenarios. In this talk, we will present emerging approaches for studying and designing model-based deep learning systems. These are methods that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, and learning from limited data. Among the applications detailed in our examples for model-based deep learning are in the areas of optimization, digital communications, and tracking in state-space models.
Biography
Nir Shlezinger is an assistant professor in the School of Electrical and Computer Engineering at Ben-Gurion University, Israel. He received his B.Sc., M.Sc., and Ph.D. degrees in 2011, 2013, and 2017, respectively, from Ben-Gurion University, Israel, all in electrical and computer engineering. From 2017 to 2019, he was a postdoctoral researcher at the Technion, and from 2019 to 2020, he was a postdoctoral researcher at the Weizmann Institute of Science, where he was awarded the FGS Prize for his research achievements. He is the recipient of the 2024 IEEE ComSoc Fred W. Ellersick Award, and the 2024 Krill Prize for outstanding young researchers. His research interests include communications, information theory, signal processing, and machine learning.
