Applied/ACMS: Difference between revisions

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*'''When:''' Fridays at 2:25pm (except as otherwise indicated)
*'''When:''' Fridays at 2:25pm (except as otherwise indicated)
*'''Where:''' 901 Van Vleck Hall
*'''Where:''' 901 Van Vleck Hall
*'''Organizers:''' [https://math.wisc.edu/staff/fabien-maurice/ Maurice Fabien], [https://people.math.wisc.edu/~rycroft/ Chris Rycroft], and [https://www.math.wisc.edu/~spagnolie/ Saverio Spagnolie],
*'''Organizers:''' [https://www.math.wisc.edu/~spagnolie/ Saverio Spagnolie], [https://people.math.wisc.edu/~rycroft/ Chris Rycroft], and [https://sites.google.com/view/laurel-ohm-math Laurel Ohm]  
*'''To join the ACMS mailing list:''' Send mail to [mailto:acms+join@g-groups.wisc.edu acms+join@g-groups.wisc.edu].
*'''To join the ACMS mailing list:''' Send mail to [mailto:acms+join@g-groups.wisc.edu acms+subscribe@g-groups.wisc.edu].


<br>   
<br>   


== Fall 2023  ==
== '''Fall 2025''' ==
== Future semesters ==
{| cellpadding="8"
! align="left" |Date
! align="left" |Speaker
! align="left" |Title
! align="left" |Host(s)
|-
|Sep 19*
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science
|Rycroft/Li
|-
|Sep 26
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|TBD
|Spagnolie
|-
|Oct 3
|
|
|
|-
|Oct 10*
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|TBD
|Rycroft
|-
|Oct 17*
|[https://www.nickderr.me/ Nick Derr] (UW)
|TBD
|Spagnolie
|-
|Oct 24
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|TBD
|Spagnolie
|-
|Oct 31
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|TBD
|Spagnolie
|-
|Nov 7*
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|TBD
|Spagnolie
|-
|Nov 14
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|TBD
|Spagnolie
|-
|Nov 21*
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|TBD
|Ohm
|-
|Nov 28
|Thanksgiving
|
|
|-
|Dec 5
|[https://mesomod.weebly.com/ Jiamian Hu] (UW; Engineering)
|TBD
|Chen
|-
|Dec 12
|[https://sites.google.com/a/brandeis.edu/tfai/home Thomas Fai] (Brandeis)
|TBD
|Rycroft
|}
''[Dates marked with an asterisk are close to weekends with a home game for the [https://uwbadgers.com/sports/football/schedule UW Badgers football team]. Hotel availability around these dates is often limited if booked on short notice.]''


*[[Applied/ACMS/Spring2024|Spring 2024]]
==Abstract==
 
<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">
== Archived semesters ==
== Archived semesters ==


*[[Applied/ACMS/Spring2025|Spring 2025]]
*[[Applied/ACMS/Fall2024|Fall 2024]]
*[[Applied/ACMS/Spring2024|Spring 2024]]
*[[Applied/ACMS/Fall2023|Fall 2023]]
*[[Applied/ACMS/Spring2023|Spring 2023]]
*[[Applied/ACMS/Spring2023|Spring 2023]]
*[[Applied/ACMS/Fall2022|Fall 2022]]
*[[Applied/ACMS/Fall2022|Fall 2022]]

Latest revision as of 02:58, 5 September 2025


Applied and Computational Mathematics Seminar


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.
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