When analyzing the relationships between multiple streams of time-series data, we often calculate crosscorrelations or deconvolutions between pairs of time series to begin to explore potential time-lagged relationships between them. However, new sensing technologies are enabling denser or larger sensor networks, and naively calculating crosscorrelations between all sensor pairs scales quadratically with the number of sensors. This is particularly problematic in ambient noise interferometry, a method by which Green’s functions of a PDE system (such as the heat equation or a wave equation) are estimated by crosscorrelations across all sensors pairs in a dense sensor network recording randomly distributed sources of energy (heat sources or vibration sources). In this talk I will show some new methods to calculate array-wide crosscorrelations that take advantage of lossy data compression to reduce data movement and computational costs by performing crosscorrelations directly on compressed data. These methods can apply to crosscorrelation of any time-series data. Over the past two decades, ambient noise interferometry has revolutionized subsurface seismic imaging for environmental, infrastructure and safety purposes, but seismic sensor networks are rapidly growing (100s-1000s of times more dense). Seismologists often use their crosscorrelation results as an input to a few types of array beamforming methods (similar to beamforming used in wireless communications and astronomy). In fact, we can calculate these final beamforming results directly from the ambient seismic noise with new linear algorithms that never explicitly calculate crosscorrelations.