The spatial structure of node locations is critical in determining the mutual interference and hence the performance of a wireless network. A common assumption is the independence of transmitter locations, and spatial Poisson point process (PPP) is ubiquitously used to model the node locations. While in many cases a PPP model leads to analytical tractability, it precludes intelligent scheduling of transmitters which introduce correlations. In this poster, I will highlight some new tools and results from stochastic geometry and point process theory to analyze functions of interference in non-Poisson wireless networks. This poster will also highlight the applications of this theory to the analysis of interference in CSMA and cognitive networks.