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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:''' [http://www.math.wisc.edu/~qinli/ Qin Li], [http://www.math.wisc.edu/~spagnolie/ Saverio Spagnolie] and [http://www.math.wisc.edu/~jeanluc Jean-Luc Thiffeault]
*'''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 2025''' ==
== Fall 2020  ==
 
{| cellpadding="8"
{| cellpadding="8"
!align="left" | date
! align="left" |Date
!align="left" | speaker
! align="left" |Speaker
!align="left" | title
! align="left" |Title
!align="left" | host(s)
! align="left" |Host(s)
|-
|-
| Sep 11
|Sep 19*
|[https://cee.stanford.edu/people/nicholas-ouellette Nick Ouellette (Stanford)]
|[https://www.anl.gov/profile/zichao-di Zichao (Wendy) Di] (Argonne National Laboratory)
|''[[Applied/ACMS/absF20#Nick Ouellette (Stanford)|Tensor Geometry in the Turbulent Cascade]]''
|Multimodal Inverse Problems and Multilevel Optimization for X-ray Imaging Science
|Jean-Luc
|Rycroft/Li
|-
|-
| Sep 18
|Sep 26
|[https://www.researchgate.net/profile/Harry_Lee24 Harry Lee (UW-Madison and UMich)]
|[https://scholar.google.com/citations?user=Imuw5CMAAAAJ&hl=en&oi=ao Pouria Behnoudfar] (UW)
|''[[Applied/ACMS/absF20#Harry Lee (UW-Madison, UMich)|Recent extension of V.I. Arnold's and J.L. Synge's mathematical theory of shear flows]]''
|TBD
|Wally
|Spagnolie
|-
|-
| Sep 25
|Oct 3
|[https://www.mtholyoke.edu/people/spencer-smith Spencer Smith (Mount Holyoke)]
|
|''[[Applied/ACMS/absF20#Spencer Smith (Mount Holyoke)|Braids on a lattice and maximally efficient mixing in active matter systems]]''
|Jean-Luc
|-
| Oct 2
|[https://zhizhenz.ece.illinois.edu/ Zhizhen Jane Zhao] (UIUC)
|''[[Applied/ACMS/absF20#Zhizhen Jane Zhao (UIUC)|Exploiting Group and Geometric Structures for Massive Data Analysis]]''
| Li & Chen
|
|
|
|
|-
|-
| Oct 9
|Oct 10*
|[https://igppweb.ucsd.edu/~mmorzfeld/ Matthias Morzfeld] (Scripps & UCSD)
|[https://www.alexandriavolkening.com Alexandria Volkening] (Purdue)
|''[[Applied/ACMS/absF20#Matthias Morzfeld (Scripps & UCSD)|What is Bayesian inference, why is it useful in Earth science and why is it challenging to do numerically?]]''
|TBD
| Chen
|Rycroft
|
|
|-
|-
| Oct 16
|Oct 17*
|[https://jingweihu-math.github.io/webpage/ Jingwei Hu] (Purdue)
|[https://www.nickderr.me/ Nick Derr] (UW)
|''[[Applied/ACMS/absF20#Jingwei Hu (Purdue)|A new stability and convergence proof of the Fourier-Galerkin spectral method for the spatially homogeneous Boltzmann equation]]''
|TBD
| Li
|Spagnolie
|
|-
|-
| Oct 23
|Oct 24
|[https://www.aos.wisc.edu/~dvimont/Home.html Dan Vimont] (UW-Madison, AOS)
|[https://cims.nyu.edu/~oneil/ Mike O'Neil] (Courant)
|''[[Applied/ACMS/absF20#Dan Vimont (UW-Madison, AOS)|Advances in Linear Inverse Modeling for Understanding Tropical Pacific Climate Variability]]''
|TBD
| Stechmann
|Spagnolie
|
|-
|-
| Oct 30
|Oct 31
|[http://www.dam.brown.edu/people/spsmith/ Sam Punshon-Smith] (Brown)
|[https://people.math.wisc.edu/~hhong78/ Hyukpyo Hong] (UW)
|''[[Applied/ACMS/absF20#Sam Punshon-Smith (Brown)|TBD]]''
|TBD
| Li
|Spagnolie
|
|-
|-
| Nov 6
|Nov 7*
|[https://www.math.uci.edu/people/yimin-zhong Yimin Zhong] (UCI, Duke)
|[https://thales.mit.edu/bush/ John Bush] (MIT)
|''[[Applied/ACMS/absF20#Yimin Zhong (UCI and Duke)|Quantitative PhotoAcoustic Tomography (PAT) with simplified PN approximation]]''
|TBD
|Li
|Spagnolie
|
|-
|-
| Nov 13
|Nov 14
|[https://www.cmu.edu/biolphys/deserno/ Markus Deserno] (CMU)
|[https://sites.google.com/andrew.cmu.edu/yukunyue/home Yukun Yue] (UW)
|''[[Applied/ACMS/absF20#Markus Deserno (CMU)|Spontaneous curvature, differential stress, and bending modulus of asymmetric lipid membranes]]''
|TBD
|Spagnolie
|Spagnolie
|-
|-
| Nov 20
|Nov 21*
|[https://www.usna.edu/Users/math/lunasin/index.php/ Evelyn Lunasin] (USNA)
|[https://jesnial.github.io/ Jessie Levillain] (CNES/INSA Toulouse)
|''[[Applied/ACMS/absF20#Evelyn Lunasin (USNA)|TBD]]''
|TBD
|Jean-Luc & Chen
|Ohm
|-
|-
| Nov 27
|Nov 28
|''Thangksgiving recess''
|Thanksgiving
|
|
|
|
|-
|-
| Dec 4
|Dec 5
||[https://www.math.arizona.edu/people/chertkov Michael Chertkov] (U. Arizona)
|[https://mesomod.weebly.com/ Jiamian Hu] (UW; Engineering)
|''[[Applied/ACMS/absF20#Michael Chertkov (U Arizona)|TBD]]''
|TBD
|Zepeda-Nunez
|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.]''
==Abstract==


== Future semesters ==
<div id="Chandler">
'''Zichao (Wendy) Di (Argonne National Laboratory)'''


*[[Applied/ACMS/Spring2021|Spring 2021]]
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/Fall2022|Fall 2022]]
*[[Applied/ACMS/Spring2022|Spring 2022]]
*[[Applied/ACMS/Fall2021|Fall 2021]]
*[[Applied/ACMS/Spring2021|Spring 2021]]
*[[Applied/ACMS/Fall2020|Fall 2020]]
*[[Applied/ACMS/Spring2020|Spring 2020]]
*[[Applied/ACMS/Spring2020|Spring 2020]]
*[[Applied/ACMS/Fall2019|Fall 2019]]
*[[Applied/ACMS/Fall2019|Fall 2019]]

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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