Date and Time: 03/14/2025, 14:00--15:00

Location: 25 Park Place, Room 1441

Colloquium: Advancing Computational Wave Imaging through Deep Learning and Wave Physics Integration

Speaker: Youzuo Lin, School of Data Science and Society

Speaker's website: http://smileunc.github.io/

Title: Advancing Computational Wave Imaging through Deep Learning and Wave Physics Integration

Abstract: Computational Wave Imaging (CWI) enables the reconstruction of hidden structures and the estimation of physical properties within a medium by analyzing propagating wave signals. This technique has critical applications in fields such as seismic exploration of the Earth's subsurface and ultrasound computed tomography in medical imaging. Traditional approaches to CWI are broadly categorized into physics-based methods and machine learning-based techniques. Physics-based methods deliver high-resolution, quantitatively accurate reconstructions of acoustic properties but are often computationally intensive and susceptible to issues of ill-posedness and nonconvexity. In contrast, machine learning approaches, particularly those leveraging deep learning, have recently emerged as powerful tools to address these challenges by offering faster, data-driven solutions. In this presentation, we introduce our recent advancements in hybrid CWI methodologies that combine fundamental wave physics with state-of-the-art machine learning techniques. We focus on the development of self-supervised learning strategies that significantly reduce computational costs while maintaining accuracy. Additionally, we explore the use of diffusion models to learn physics-consistent solution spaces, further enhancing the robustness and efficiency of CWI solutions. We will demonstrate the effectiveness of these approaches through applications in subsurface geophysics and medical ultrasound imaging, highlighting their potential for broad, real-world impact.

Speaker's biography: Youzuo Lin is an Associate Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. Previously, he served as a Senior Scientist at Los Alamos National Laboratory. He earned his Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010. Youzuo’s research focuses on scientific machine learning methods and their applications, particularly in inverse problems, computational wave imaging, ultrasound tomography, geophysical inversion, and UAV image analysis. He has published over 100 articles in leading journals and conference proceedings and is a co-inventor on several U.S. patents related to ultrasound imaging techniques.

Host: Yi Jiang (yjiang12@gsu.edu)