My group at Princeton explores imaging and computer vision approaches that allow computers to see and understand what seems invisible today — enabling super-human capabilities for the cameras in our vehicles, personal devices, microscopes, telescopes, and the instrumentation we use for fundamental research in physics. This includes today's capture and vision challenges, including harsh environmental conditions, e.g. imaging under ultra-low or high illumination or computer vision through dense fog, rain, and snow, imaging at ultra-fast or slow time scales, freezing light in motion, imaging at extreme scene scales, from super-resolution microscopy to kilometer-scale depth sensing, and imaging via proxies using nearby object surfaces as sensors instead. Researching vision systems end-to-end, my work lies at the intersection of optics, machine learning, optimization, computer graphics, and computer vision.