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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">This is an announcement of Olivia </span><span style="font-family: Arial, sans-serif; color: rgb(60, 64, 67); letter-spacing: 0.15pt; background-color: white;" class="">Tsang</span><span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">’s
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">===============================================<o:p class=""></o:p></span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Candidate: Olivia Tsang</span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Date: Wednesday, May 08, 2024</span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Time: 12 pm CT</span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Location: JCL 346</span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Title: </span><span style="color: rgb(33, 33, 33); font-family: Arial, sans-serif;" class="">Solving the Inverse Scattering Problem with Machine Learning-Based Iterative Refinement</span></div>
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<span style="color: rgb(33, 33, 33); font-family: Arial, sans-serif;" class="">Abstract: </span><span style="color: rgb(33, 33, 33); font-family: Arial, sans-serif; font-size: 11pt;" class="">Acoustic and electromagnetic waves can be used to probe materials
in a non-destructive manner, such as in seismic imaging, radar detection, and medical imaging. However, it can be challenging to recover an image of the object from measurements of the outgoing wave. This is known as an inverse scattering problem, and it is
difficult because the wave scattering process is in general nonlinear and non-local. When low-frequency probe waves are used, the scattering process is nearly linear but ignores features smaller than the diffraction limit (i.e., half a wavelength long). A
well-established approach, known as “Recursive Linearization,” uses multi-frequency data to break the recovery process into a sequence of linear inverse problems, which enables stable and high-resolution reconstructions.</span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Inspired by the Recursive Linearization idea, we propose a machine learning-based method that explicitly breaks the reconstruction process into multiple refinement steps. Methods
built on Recursive Linearization tend to be very computationally expensive, while existing machine learning-based methods tend to struggle in the strongly nonlinear scattering regime. We find that our method outperforms existing machine learning approaches,
as well as new baselines that omit the iterative refinement formulation.<o:p class=""></o:p></span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">This work has been done jointly with Owen Melia, Rebecca Willett, Vasilis Charisopoulos, Jeremy Hoskins, and Yuehaw Khoo.</span></div>
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<span style="font-family: Arial, sans-serif; color: rgb(33, 33, 33);" class="">Advisors: Rebecca Willett</span></div>
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<div style="margin: 0in;" class=""><font color="#212121" face="Arial, sans-serif" class=""><span style="font-size: 11pt;" class="">Committee Members: Rebecca Willett, Risi Kondor, and </span><span style="caret-color: rgb(33, 33, 33); font-size: 14.666666984558105px;" class="">Yuehaw
Khoo</span></font></div>
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