Speaker: Yonina Eldar, Aoun Chair Professor of Electrical and Computer Engineering at Northeastern University and the Dorothy and Patrick Gorman Professorial Chair of Mathematics and Computer Science at the Weizmann Institute
Date & Time: January 23th 2026, Friday at 10 am
Venue: 370 Jay Street, 8th Floor, Room 825, Brooklyn, NY 11201
Abstract: Deep neural networks have achieved unprecedented performance gains across numerous real-world signal and image processing tasks. However, their widespread adoption and practical deployment are often limited by their black-box nature – characterized by a lack of interpretability and a reliance on large training datasets.
In contrast, traditional approaches in signal processing, sensing, and communications have long leveraged classical statistical modeling techniques, which incorporate mathematical formulations based on underlying physical principles, prior knowledge, and domain expertise. While these models offer valuable insights, they can be overly simplistic and sensitive to inaccuracies, leading to suboptimal performance in complex or dynamic real-world scenarios.
This talk explores various approaches to model-based learning which merge parametric models with optimization tools and classical algorithms to create efficient, interpretable deep networks that require significantly smaller training datasets. We demonstrate the advantages of this approach through applications in image deblurring, image separation, super-resolution for ultrasound and microscopy, radar for clinical diagnostics, efficient communication systems, low-power sensing devices, and more. Additionally, we present theoretical results that establish the performance advantages of model-based deep networks over purely data-driven black-box methods.
Bio: Yonina Eldar is the Aoun Chair Professor of Electrical and Computer Engineering at Northeastern University and the Dorothy and Patrick Gorman Professorial Chair of Mathematics and Computer Science at the Weizmann Institute where she founded and heads the Signal Acquisition Modeling Processing and Learning Lab (SAMPL) and the Center for Biomedical Engineering. She is also a Visiting Professor at MIT and Princeton, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford. She is a member of the Israel Academy of Sciences and Humanities and of the Academia Europaea, an IEEE Fellow and a EURASIP Fellow. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering from Tel-Aviv University, and the Ph.D. degree in electrical engineering and computer science from MIT. She has received many awards for excellence in research and teaching, including the Israel Prize (2025), Landau Prize (2024), IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), and the Award for Women with Distinguished Contributions. She was selected as one of the 50 most influential women in Israel, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing, a member of several IEEE Technical Committees and Award Committees, and heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.
Ken Perlin, a professor in the Department of Computer Science at New York University, directs the Future Reality Lab, and is a participating faculty member at NYU MAGNET. His research interests include future reality, computer graphics and animation, user interfaces and education. He is chief scientist at Parallux and Tactonic Technologies. He is an advisor for High Fidelity and a Fellow of the National Academy of Inventors. He received an Academy Award for Technical Achievement from the Academy of Motion Picture Arts and Sciences for his noise and turbulence procedural texturing techniques, which are widely used in feature films and television, as well as membership in the ACM/SIGGRAPH Academy, the 2020 New York Visual Effects Society Empire Award the 2008 ACM/SIGGRAPH Computer Graphics Achievement Award, the TrapCode award for achievement in computer graphics research, the NYC Mayor’s award for excellence in Science and Technology and the Sokol award for outstanding Science faculty at NYU, and a Presidential Young Investigator Award from the National Science Foundation. He serves on the Advisory Board for the Centre for Digital Media at GNWC. Previously he served on the program committee of the AAAS, was external examiner for the Interactive Digital Media program at Trinity College, general chair of the UIST2010 conference, directed the NYU Center for Advanced Technology and Games for Learning Institute, and has been a featured artist at the Whitney Museum of American Art. He received his Ph.D. in Computer Science from NYU, and a B.A. in theoretical mathematics from Harvard. Before working at NYU he was Head of Software Development at R/GREENBERG Associates in New York, NY. Prior to that he was the System Architect for computer generated animation at MAGI, where he worked on TRON.