Guest Lecturer Dr. Ulugbek Kamilov, Computational Imaging: Reconciling Models and Learning

There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquisition. There are currently two distinct viewpoints on addressing such information loss: modelbased and learningbased. Modelbased methods leverage analytical signal properties (such as sparsity) and often come with theoretical guarantees and insights. Learningbased methods leverage flexible representations (such as convolutional neural nets) for best empirical performance through t