Efficient Machine Learning Systems for Signal Processing

In this episode of the IEEE Signal Processing Society podcast, Nir Shlezinger from Ben-Gurion University and Yonina C. Eldar from the Weizmann Institute of Science discuss the design of machine learning systems that are inherently efficient.  Nir Shlezinger and Yonina C. EldarNir Shlezinger is an Assistant Professor in the School of Electrical and Computer Engineering at Ben-Gurion University of the Negev, Israel. His research spans signal processing, machine learning, and communications. He has been recognized with several prestigious awards, including the IEEE Communications Society Fred W. Ellersick Prize and the 2024 Krill Award.Yonina C. Eldar is a Professor at the Weizmann Institute of Science, where she heads the Center for Biomedical Engineering and Signal Processing. She is also a member of the Israel Academy of Sciences and Humanities and an IEEE Fellow.In this episode, Dr. Shlezinger and Dr. Eldar engage in a rich discussion on model-based deep learning—an approach that combines classical signal processing principles with modern data-driven techniques. This framework promotes efficiency not only through computational improvements, but by designing learning algorithms that naturally align with physical models and mathematical structures. They explore the key principles behind this methodology, its practical advantages, and its growing impact across a range of signal processing applications.