[Eoas-seminar] [Seminar-announce] Scientific Computing Colloquium with Simone Brugiapaglia

[eoas-seminar at lists.fsu.edu]

"From compression to depth: generative compressive sensing and deep greedy unfolding for signal reconstruction"

Simone Brugiapaglia
Department of Mathematics and Statistics,
Concordia University

Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.

NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intended for external (non-departmental) participants only.

https://fsu.zoom.us/j/94273595552
Meeting # 942 7359 5552

🎦 Colloquium recordings will be made available here, https://www.sc.fsu.edu/colloquium<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C3a41d7189bee4ef21b3208de61c824c1%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639055709391525182%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=d6vxX6HSZuZqAhLWpS5dXE1Z1uEnICfpYSVDoHLrLSk%3D&reserved=0>


Wednesday, Feb 4, 2026, Schedule:

* 3:00 to 3:30 PM Eastern Time (US and Canada)
☕ Nespresso & Teatime - 417 DSL Commons

* 3:30 to 4:30 PM Eastern Time (US and Canada)
🕟 Colloquium - 499 DSL Seminar Room

Abstract:
Since its inception in the early 2000s, compressive sensing has become a well-established paradigm for efficient signal recovery, with applications ranging from medical imaging to scientific computing. More recently, data-driven reconstruction methods based on deep neural networks have attracted considerable attention and shown great promise as an alternative approach. In this talk, we will review recent progress in signal reconstruction techniques that combine principles from compressive sensing and deep learning. First, we will discuss recent advances in generative compressive sensing, where the traditional sparsity prior is replaced by the assumption that the signal to be reconstructed lies in the range of a deep generative neural network. Second, we will explore deep greedy unfolding, which involves designing deep neural network architectures by "unrolling" the iterations of a sparse recovery algorithm onto the layers of a trainable neural network. In both cases, we will present numerical results in tandem with theoretical guarantees.


Additional colloquium details can be found here,
https://www.sc.fsu.edu/news-and-events/colloquium/1905-colloquium-with-simone-brugiapaglia-2026-02-04<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1905-colloquium-with-simone-brugiapaglia-2026-02-04&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C3a41d7189bee4ef21b3208de61c824c1%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639055709391557467%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=6by3kc8B%2B%2BQq7aPGI3jabL2JDzZlXGcY%2B2gt7RReTis%3D&reserved=0>

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