AMS Sectional Meeting AMS Special Session
Current as of Sunday, April 14, 2024 03:30:04
2024 Spring Eastern Sectional Meeting
- Howard University, Washington, DC
- April 6-7, 2024 (Saturday - Sunday)
- Meeting #1194
Associate Secretary for the AMS Scientific Program:
Steven H Weintraub, Lehigh University shw2@lehigh.edu
Special Session on Recent Progress on Model-Based and Data-Driven Methods in Inverse Problems and Imaging
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Saturday April 6, 2024, 8:30 a.m.-11:00 a.m.
Special Session on Recent Progress on Model-Based and Data-Driven Methods in Inverse Problems and Imaging, I
Inverse problems involve deducing causative factors from observed data, a fundamental task in various scientific domains, with particular prevalence in imaging sciences and technologies. Traditional approaches to addressing inverse problems delve into the intricate connection between causal factors and observations as is dictated by physical models, leveraging diverse mathematical tools like partial differential equations, functional analysis, optimization, numerical analysis, and probability theory. On the other hand, recent decades have ushered in a noteworthy trend of utilizing data-driven techniques for solving inverse problems. In contrast to classical model-based methods, data-driven methods provide fresh insights into overcoming critical challenges inherent in inverse problems, such as dimensionality issues and ill-posedness, etc.This session's primary objective is to convene mathematicians specializing in inverse problems and imaging. The event aims to facilitate discussions on the recent advancements in both model-based and data-driven methods, fostering the exchange of knowledge and ideas. The session is expected to promote development of novel ideas and research collaborations within this dynamic field.
LKH 257, Alain Locke Hall
Organizers:
Yimin Zhong, Auburn University yzz0225@auburn.edu
Yang Yang, Michigan State University
Junshan Lin, Auburn University
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8:30 a.m.
The Anisotropic Transmission Eigenvalue Problem with a Conductive Boundary
Victor Giovanni Hughes*, Purdue University
(1194-35-34971) -
9:00 a.m.
Learning Dynamics guided by Inverse Mean-field Game Problems
Rongjie Lai*, Purdue University
(1194-49-34976) -
9:30 a.m.
Break -
10:00 a.m.
Regularization of the Factorization Method with Applications
Isaac Harris*, Purdue University
(1194-35-34255) -
10:30 a.m.
Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation
Vishwas Rao, Argonne National Laboratory
Arvind Krishna Saibaba, North Carolina State University
Amit Subrahmanya*, Virginia Tech
(1194-35-34560)
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8:30 a.m.
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Saturday April 6, 2024, 3:00 p.m.-5:00 p.m.
Special Session on Recent Progress on Model-Based and Data-Driven Methods in Inverse Problems and Imaging, II
Inverse problems involve deducing causative factors from observed data, a fundamental task in various scientific domains, with particular prevalence in imaging sciences and technologies. Traditional approaches to addressing inverse problems delve into the intricate connection between causal factors and observations as is dictated by physical models, leveraging diverse mathematical tools like partial differential equations, functional analysis, optimization, numerical analysis, and probability theory. On the other hand, recent decades have ushered in a noteworthy trend of utilizing data-driven techniques for solving inverse problems. In contrast to classical model-based methods, data-driven methods provide fresh insights into overcoming critical challenges inherent in inverse problems, such as dimensionality issues and ill-posedness, etc.This session's primary objective is to convene mathematicians specializing in inverse problems and imaging. The event aims to facilitate discussions on the recent advancements in both model-based and data-driven methods, fostering the exchange of knowledge and ideas. The session is expected to promote development of novel ideas and research collaborations within this dynamic field.
LKH 257, Alain Locke Hall
Organizers:
Yimin Zhong, Auburn University yzz0225@auburn.edu
Yang Yang, Michigan State University
Junshan Lin, Auburn University
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3:00 p.m.
Analysis and reduction of metal artifacts in X-ray tomography
Yiran Wang*, Emory University
(1194-35-34937) -
3:30 p.m.
Fast Imaging of Local Defects in Complex Periodic Media
Fioralba Cakoni, Rutgers University, New Brunswick
Houssem Haddar, INRIA, France
Thi-Phong Nguyen*, New Jersey Institute of Technology
(1194-35-34951) -
4:00 p.m.
Exact inversion of an integral transform arising in Compton camera imaging
Fatma Terzioglu*, North Carolina State University
(1194-44-34819) -
4:30 p.m.
Fast imaging of point-like radiating sources using single-frequency data
Dinh-Liem Nguyen*, Kansas State University
(1194-65-34820)
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3:00 p.m.
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Sunday April 7, 2024, 8:30 a.m.-11:00 a.m.
Special Session on Recent Progress on Model-Based and Data-Driven Methods in Inverse Problems and Imaging, III
Inverse problems involve deducing causative factors from observed data, a fundamental task in various scientific domains, with particular prevalence in imaging sciences and technologies. Traditional approaches to addressing inverse problems delve into the intricate connection between causal factors and observations as is dictated by physical models, leveraging diverse mathematical tools like partial differential equations, functional analysis, optimization, numerical analysis, and probability theory. On the other hand, recent decades have ushered in a noteworthy trend of utilizing data-driven techniques for solving inverse problems. In contrast to classical model-based methods, data-driven methods provide fresh insights into overcoming critical challenges inherent in inverse problems, such as dimensionality issues and ill-posedness, etc.This session's primary objective is to convene mathematicians specializing in inverse problems and imaging. The event aims to facilitate discussions on the recent advancements in both model-based and data-driven methods, fostering the exchange of knowledge and ideas. The session is expected to promote development of novel ideas and research collaborations within this dynamic field.
LKH 257, Alain Locke Hall
Organizers:
Yimin Zhong, Auburn University yzz0225@auburn.edu
Yang Yang, Michigan State University
Junshan Lin, Auburn University
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8:30 a.m.
Uncertainty Quantification for Ultrasound Modulated Bioluminescence Tomography in Diffusive Regime
Tianyu Yang*, Michigan State University
(1194-35-34978) -
9:00 a.m.
On inverse problems to mean field game systems
Kui Ren*, Columbia University
(1194-35-34287) -
9:30 a.m.
Break -
10:00 a.m.
DNN-oriented indicator method for inverse scattering problems using partial data
Jiguang Sun*, Michigan Technological University
(1194-35-34680) -
10:30 a.m.
Multiscale hierarchical image decomposition for multiplicative noise
Joel Barnett, University of California, Los Angeles
Wen Li*, Fordham University
Elena Resmerita, Institute of Mathematics, The University of Klagenfurt (AAU)"
Luminita Vese, University of California Los Angeles
(1194-65-35548)
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8:30 a.m.
Inquiries: meet@ams.org