compressed sensing or compressive sampling (cs) is a signal processing technique for efficiently acquiring and reconstructing sparse signals by solving underdetermined linear systems. in practice, cs needs to be accompanied by a quantization process. that is, after sampling the signals, we represent the measurements using discrete data, e.g. 0s and 1s, and recover the signals from the quantized measurements. in this talk, i will discuss how to extend the noise-shaping quantization methods beyond the case of gaussian measurements to structured random measurements, including random partial fourier and random partial circulant measurements. this is joint work with rayan saab