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In 2017, a new magnetic resonance imaging (MRI) device by General Electric and Siemens entered the marketplace with an advertised 10-fold speedup over traditional MRI and the potential to impact 80 million MRI scans annually. This talk will discuss the applications and some of the mathematics behind this advance, coming from the field of “compressed sensing” that leverages higher-dimensional geometry in novel ways. A key role in this development was played by many researchers who have been at Stanford in fields ranging from electrical engineering and radiology to applied mathematics and statistics. We hope to highlight some of those researchers and their work.