Computational Imaging Across Modalities 

Computational imaging is transforming how scientists visualize, interpret, and understand complex systems, ranging from medical diagnostics to earth and material sciences. This workshop will bring together researchers across disciplines to explore the latest advances at the intersection of physics-based modeling, machine learning, and computational imaging. We will highlight a wide spectrum of imaging modalities, spanning wave-based techniques (seismic, acoustic, ultrasound), nuclear and magnetic methods (CT, MRI, PET, and beyond). By uniting these communities, the workshop will identify shared challenges—such as ill-posedness, data scarcity, and generalization—and discuss hybrid solutions that merge physical principles with data-driven learning to achieve robust, interpretable, and efficient reconstructions. Beyond technical presentations, the workshop will feature community-driven activities, including open challenges, dataset and benchmark discussions, and interactive tutorials on foundation and hybrid models for imaging. This engagement will encourage collaboration across academia, national laboratories, and industry, fostering new partnerships and innovations in computational imaging research. By emphasizing both scientific innovation and community building, this workshop aims to define a shared roadmap for the future of computational imaging—where physics-informed learning enables discovery across modalities, scales, and scientific domains.