Applied/ACMS: Difference between revisions
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|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory) | |[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory) | ||
| | |Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science | ||
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==Abstract== | ==Abstract== | ||
<div id="Chandler"><div id="Fraser"><div id="Luedtke"><div id="Zhdankin"><div id="Boffi"><div id="Shankar"><div id="Loevbak"> | <div id="Chandler"> | ||
'''Zichao (Wendy) Di (Argonne National Laboratory)''' | |||
Title: Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science | |||
X-ray imaging experiments generate vast datasets that are often incomplete or ill-posed when considered in isolation. One way forward is multimodal data analysis, where complementary measurement modalities are fused to reduce ambiguity and improve reconstructions. A key question, both mathematically and practically, is how to identify which modalities to combine and how best to integrate them within an inverse problem framework. | |||
A second line of work focuses on the computational challenge: even for single-modality inverse problems, the resulting optimization problems are large-scale, nonlinear, and nonconvex. Here, I will discuss multilevel optimization and stochastic sampling strategies that accelerate convergence by exploiting hierarchical structure in both parameter and data spaces. | |||
Although developed separately, these two directions point toward a common goal: building scalable, optimization-based frameworks that make the best use of diverse data to enable new discoveries in X-ray imaging science.<div id="Fraser"><div id="Luedtke"><div id="Zhdankin"><div id="Boffi"><div id="Shankar"><div id="Loevbak"> | |||
<div id="Lu"><div id="Vogman"><div id="Cockburn"> | <div id="Lu"><div id="Vogman"><div id="Cockburn"> | ||
== Archived semesters == | == Archived semesters == | ||
Revision as of 02:58, 5 September 2025
Applied and Computational Mathematics Seminar
- When: Fridays at 2:25pm (except as otherwise indicated)
- Where: 901 Van Vleck Hall
- Organizers: Saverio Spagnolie, Chris Rycroft, and Laurel Ohm
- To join the ACMS mailing list: Send mail to acms+subscribe@g-groups.wisc.edu.
Fall 2025
| Date | Speaker | Title | Host(s) |
|---|---|---|---|
| Sep 19* | Zichao (Wendy) Di (Argonne National Laboratory) | Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science | Rycroft/Li |
| Sep 26 | Pouria Behnoudfar (UW) | TBD | Spagnolie |
| Oct 3 | |||
| Oct 10* | Alexandria Volkening (Purdue) | TBD | Rycroft |
| Oct 17* | Nick Derr (UW) | TBD | Spagnolie |
| Oct 24 | Mike O'Neil (Courant) | TBD | Spagnolie |
| Oct 31 | Hyukpyo Hong (UW) | TBD | Spagnolie |
| Nov 7* | John Bush (MIT) | TBD | Spagnolie |
| Nov 14 | Yukun Yue (UW) | TBD | Spagnolie |
| Nov 21* | Jessie Levillain (CNES/INSA Toulouse) | TBD | Ohm |
| Nov 28 | Thanksgiving | ||
| Dec 5 | Jiamian Hu (UW; Engineering) | TBD | Chen |
| Dec 12 | Thomas Fai (Brandeis) | TBD | Rycroft |
[Dates marked with an asterisk are close to weekends with a home game for the UW Badgers football team. Hotel availability around these dates is often limited if booked on short notice.]
Abstract
Zichao (Wendy) Di (Argonne National Laboratory)
Title: Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science
X-ray imaging experiments generate vast datasets that are often incomplete or ill-posed when considered in isolation. One way forward is multimodal data analysis, where complementary measurement modalities are fused to reduce ambiguity and improve reconstructions. A key question, both mathematically and practically, is how to identify which modalities to combine and how best to integrate them within an inverse problem framework.
A second line of work focuses on the computational challenge: even for single-modality inverse problems, the resulting optimization problems are large-scale, nonlinear, and nonconvex. Here, I will discuss multilevel optimization and stochastic sampling strategies that accelerate convergence by exploiting hierarchical structure in both parameter and data spaces.
Although developed separately, these two directions point toward a common goal: building scalable, optimization-based frameworks that make the best use of diverse data to enable new discoveries in X-ray imaging science.Archived semesters
- Spring 2025
- Fall 2024
- Spring 2024
- Fall 2023
- Spring 2023
- Fall 2022
- Spring 2022
- Fall 2021
- Spring 2021
- Fall 2020
- Spring 2020
- Fall 2019
- Spring 2019
- Fall 2018
- Spring 2018
- Fall 2017
- Spring 2017
- Fall 2016
- Spring 2016
- Fall 2015
- Spring 2015
- Fall 2014
- Spring 2014
- Fall 2013
- Spring 2013
- Fall 2012
- Spring 2012
- Fall 2011
- Spring 2011
- Fall 2010
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