The MIC program seeks to train Ph.D. students to bridge the gap between theory and practice by providing insights into the various interfaces among mathematical theories, scientific computation and visualization. Imaging problems can be formulated as inverse problems that are intrinsically nonlinear. Finding solutions with practical significance and value requires an in-depth understanding of the underlying physical phenomena with data acquisition systems as well as implementation details of image reconstruction algorithms. Experience over the last three decades has shown that the symbiotic interplay among theoretical mathematics, computational mathematics, and experiments is crucial for understanding and solving these nonlinear problems in practice. Therefore, we brings together a diverse group of researchers from different disciplines including mathematics, biomedical engineering, neuroscience, and radiology to explore innovative approaches.