The accurate and efficient identification of microbial organism such as viruses and bacteria has mounting importance in the fields of health care, environmental monitoring, and defense. As an example, sepsis from bacterial infection is currently the 11th leading cause of death in the United States. However, current microbial detection strategies are cost-prohibitive, time-consuming and inevitably use unique sensors that are specific to each species to be detected. In this thesis we present a novel microbial sensing platform capable of both detecting the presence and estimating the concentration of microbial organisms in an infectious sample using a small number of random DNA probes. Our Universal Microbial Diagnostics (UMD) platform leverages the theory of sparse signal recovery (compressive sensing) to stably identify the composition of a sample containing several bacteria from a potentially large library of target bacteria. We experimentally validate UMD in vitro using a set of random sloppy molecular beacons to recover pathogenic bacteria without DNA amplification. We also evaluate the average performance of UMD in silico for genus and species level identification of 38 common human pathogens. A particularly promising property of UMD for health care, environmental monitoring, and defense applications is that a fixed set of random measurement probes are universal in the sense that they can characterize novel organisms not present in the target library.