Abstract: New-generation imaging systems have enabled high-dimensional signal sensing employing optical coding theory and high-performance inverse problems algorithms. State-of-the-art methods employ deep learning approaches to properly design optical systems jointly with neural networks that perform decoding tasks. This talk presents a comprehensive overview of deep optical coding design and novel learning paradigms providing new insights on optimal optical coding performance such as regularization techniques and knowledge distillation strategies