Abstract: Computational imaging is permeating cameras and microscopes across many scientific applications, enabling new high-resolution and multi-dimensional measurement capabilities (e.g. phase, 3D, hyperspectral). But many methods require acquisition of multiple images to reconstruct this new information, limiting their applicability for live dynamic samples, where motion blur can cause severe artifacts. This talk will describe new space-time algorithms that correct for motion artifacts and solve for dynamics, with imperfect optical systems or approximate forward models. Traditional model-based image reconstruction algorithms work together with neural networks to optimize the inverse problem solver and the data capture strategy.