Recent observation by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and by Advanced Virgo (aVirgo) detectors of mergers of binary black hole and binary neutron star systems have opened the field of multi-messenger astrophysics. They have sparked a very broad range of studies, from new tests of General Relativity and the measurement of the Hubble constant to constraints on the equation of state in neutron stars and studies of the r-process of heavy element production in the binary neutron star merger. Adding the gravitational wave signals from all such binaries in the universe leads to a stochastic gravitational wave background (SGWB). The above discoveries have enabled a robust estimate of this background, indicating that it is within reach of the upcoming observation runs of aLIGO and aVirgo. The SGWB may well be the next new type of gravitational-wave signal to be discovered by these detectors, with the discovery coming potentially as early as 2019.  This project aims to measure (and detect) the SGWB using data from upcoming observation runs of aLIGO and aVirgo, improving the sensitivity by up to 100x relative to the most recent results. The project will support involvement of graduate and undergraduate students in research at the frontier of the nascent field of multi-messenger astrophysics, as well as activities designed to share the excitement of this new field with broader audience.More specifically, using cross-correlation techniques applied to aLIGO and aVirgo data, the frequency content and temporal structure of the SGWB will be measured. Bayesian parameter estimation framework will then use this information to estimate the contributions of various astrophysical and cosmological SGWB models, as well as to identify and remove environmental contamination, such as due to the Schumann magnetic resonances. The results of this analysis are expected to place stringent constraints on the formation and evolution of compact binary systems, hence illuminating the evolution of the observed structure in the universe. Furthermore, these results will have the potential to constrain cosmological SGWB models, such as inflationary or cosmic (super)string models, and therefore probe the physics of fundamental interactions at very high energies, unachievable in laboratories. Similar cross correlation techniques will also be used to develop new searches for long transient signals, lasting from hours to days or weeks, which are expected in multiple models of neutron stars and are of particular interest for studying the remnants of binary neutron star mergers. If detected, such a signal would provide information about the physics processes driving Gamma Ray Bursts and about the high-density state of nuclear matter in neutron stars. To further improve the sensitivity of these searches, deep machine learning techniques will be used to remove the environmental contamination from the aLIGO and aVirgo gravitational-wave data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.