The Hopkins Computational Imaging Group (HCI) develops advanced algorithms and mathematical frameworks for computational imaging, computer vision, and image processing. Our research sits at the intersection of AI & ML, mathematics, and data science, with a particular emphasis on integrating modern deep learning tools—such as diffusion models and neural fields—with physical models and principled inference techniques. We aim to leverage this intersection to design next-generation imaging systems that are not only more accurate and intelligent, but also interpretable and robust. Our applications span optical microscopy, magnetic resonance imaging, computed tomography, and black-hole interferometry.

Topics of Interest: Inverse Problems, Scientific Imaging, Computational Photography, Generative Models, Convex/Nonconvex Optimization & Sampling as Optimization