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Signal processing is multidisciplinary in nature. It provides mathematical analysis and computational operations on a wide range of signal or information types in diverse application fields that are typically classified as different technical areas. The idea of benefiting from research methodologies and techniques across disparate but related signal processing technical areas has been embraced by numerous signal processing researchers. This summer, I had the opportunity to co-organize the Banff Workshop on Multimedia, Mathematics, and Machine Learning with Prof. Rabab Ward, where a group of distinguished researchers and educators worldwide were invited. Many of the invitees were pursuing research that touched on not just one but multiple signal processing technical areas, and thus were able to discuss common underlying principles and methods for a wide range of media signal processing applications, and benefit from "cross-pollination" over these fields. Several talks focused on cross-fertilization between different signal processing areas and these led to many interesting discussions at the workshop. Such talks included "Mobile Image Matching --- Recognition Meets Compression" by B. Girod (Stanford University), "Machine Hearing (vs. Machine Vision)" by D. Lyon (Google Research), and "Statistical Methods for Image, Speech, and Language Processing: Achievements and Open Problems" by H. Ney (RWTH Aachen University).