the famous shannon-nyquist theorem has become a landmark in the development of digital signal and image processing. however, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. consequently, conversion to digital has become a serious bottleneck. furthermore, the resulting digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power. in the context of medical imaging sampling at high rates often translates to high radiation dosages, increased scanning times, bulky medical devices, and limited resolution. in this talk, we present a framework for sampling and processing a large class of wideband analog signals at rates far below nyquist in space, time and frequency, which allows to dramatically reduce the number of antennas, sampling rates and band occupancy. our framework relies on exploiting signal structure and the processing task. we consider applications of these concepts to a variety of problems in communications, radar and ultrasound imaging and show several demos of real-time sub-nyquist prototypes including a wireless ultrasound probe, sub-nyquist mimo radar, super-resolution in microscopy and ultrasound, cognitive radio, and joint radar and communication systems. we then discuss how the ideas of exploiting the task, structure and model can be used to develop interpretable model-based deep learning methods that can adapt to existing structure and are trained from small amounts of data. these networks achieve a more favorable trade-off between increase in parameters and data and improvement in performance, while remaining interpretable.