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Hyperspectral imaging is used for a wide range of tasks from medical diagnostics to crop monitoring, but traditional imagers are prohibitively expensive for widespread use. This research strives to democratize hyperspectral imaging by using machine learning to reconstruct hyperspectral volumes from snapshot imagers. I propose a tunable lens with varying amounts of defocus paired with 31-channel spectral filter array mounted on a CMOS camera. These images are then fed into a reconstruction network that aims to recover the full 31-channel hyperspectral volume from a few encoded images with different amounts of defocus.