Thursday Colloquium

Thursday, March 5th, 2026 (in conjunction with the Distinguished Lecture Series) 

Speaker: Chenyang Xu (Princeton)

Title: K-stability of Fano varieties Lecture 3

Abstract. K-stability was first defined in complex geometry by Tian in late 90s and then reformulated by Donaldson in algebraic terms, to characterize the existence of Kähler-Einstein metrics on Fano varieties. In the last decade, a purely algebro-geometric theory has been developed. The theory combines deep techniques in higher dimensional algebraic geometry, with a circle of new perspectives from K-stability theory. Major outputs then include a moduli theory for Fano varieties, a new stability theory of singularities, as well as many new examples of Kähler-Einstein Fano varieties etc..

In my first lecture, I will give a survey of the K-stability theory, which is targeted to general audience. The in the two other lectures, I will discuss more details of various parts of the theory.

Thursday, March 26th, 2026 (in conjunction with the Data Theory Series) 

Speaker: Yonina Eldar (Northeastern University)

Title: Model-Based Deep Learning for Sensing and Imaging: Efficient and Interpretable AI

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.

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.

Thursday, April 16th, 2026

Speaker: Mustafa Hajij (University of San Francisco)

Title: TBA

Abstract. TBA

Thursday, April 30th, 2026

Speaker: Hrushikesh Mhaskar (Claremont Graduate University)

Title: TBA

Abstract. TBA