[CS] Olivia Tsang Candidacy Exam/Jan 22, 2026

[via cs]

This is an announcement of Olivia Tsang's Candidacy Exam.
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Candidate: Olivia Tsang

Date: Thursday, January 22, 2026

Time: 10 am CST

Location: JCL 298

Title: A Model-Guided Neural Network Method for the Inverse Scattering Problem

Abstract: Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased inference speed and flexibility in capturing prior knowledge of imaging targets relative to classical optimization-based approaches; however, they perform poorly in regimes where the scattering behavior is highly nonlinear. A key limitation is that ML methods struggle to incorporate the physics governing the scattering process, which are typically inferred implicitly from the training data or loosely enforced via architectural design.

In this talk, I present a method that endows a machine learning framework with explicit knowledge of problem physics, in the form of a differentiable solver representing the forward model. The proposed method progressively refines reconstructions of the scattering potential using measurements at increasing wave frequencies, following a classical strategy to stabilize recovery. Empirically, we find that that the new method provides high-quality reconstructions at a fraction of the computational or sampling costs of competing approaches.

Advisors: Rebecca Willett

Committee Members: Risi Kondor, Rebecca Willett, and Yuehaw Khoo