ZOOM LINKMeeting ID: 937 4316 0035Passcode: 411385 Computational imaging jointly designs hardware and algorithms to push beyond the classical limits of imaging, enabling measurement of new quantities (eg 3D, phase, and super-resolution) with simple, inexpensive hardware. In this talk, I show recent advances that push both spatial and temporal throughput for 2D and 3D fluorescence microscopy. First, I will describe a diffractive, multiplexed microscope that uses engineered point spread functions and a multi-sensor array to achieve gigapixel-scale imaging at video rates, enabling micrometer-resolution imaging over centimeter-scale fields of view for dynamic biological systems. Second, I will introduce a neural space-time model that jointly reconstructs images and motion from sequential measurements, eliminating motion artifacts while recovering sample dynamics without training data or priors. Together, these approaches illustrate a shift from static imaging toward high-throughput, dynamic measurement, opening new opportunities for observing complex biological processes across scales. Hosted by Professor Irene Georgakoudi