Applied/ACMS

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Revision as of 02:58, 5 September 2025 by Rycroft (talk | contribs) (Wendy Di title+abstract.)
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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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