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In microscopy, the proliferation of digital data resulting from an increasing number of sensors, and automated data capture, has created opportunities for rich characterization of structures in materials. Whereas traditional characterization involved small numbers of 2-D images that were hand analyzed, modern techniques can capture image sequences involving hundreds of individual images, leading to characterization over "large volumes," as compared with traditional approaches. Multiple sensors have allowed for multimodal data collection; robotics has allowed for automated data preparation and collection; and increasing storage capacities have lead to large datasets. The availability of this type of data, which primarily involves imaging modalities, has created an opportunity for modern Computational Imaging (CI) methods to be applied to obtain vast improvements in the quality of micro- and nano-structure analyses as well as their complexity. Traditionally, quantitative analysis of microscope imagery has relied upon forward modeling for testing hypotheses. CI, with its emphasis on inversion methods, represents a largely unexplored methodology in this field. This symposium brings together CI researchers from Imaging Science, Materials Science, and other fields who are applying techniques such as statistical image processing, model-based regularization, sparsity, denoising methods, optics, and the integration of the physics of structure formation towards advancing the science of microscopy.