Abstract: The eruptions, collisions and explosions of stars drive the universe’s chemical and dynamical evolution. The upcoming Large Synoptic Survey Telescope will drastically increase the discovery rate of these transient phenomena, bringing time-domain astrophysics into the realm of “big data.” With this transition comes the important question: how do we classify transient events and separate the interesting “needles” from the “haystack” of objects? In this talk, I will discuss efforts to discover and classify unexpected phenomena using semi-supervised machine learning techniques. I will highlight one particularly exciting “needle”: the electromagnetic counterparts of neutron star mergers. Finally, I will discuss how we can use statistical methods to understand the underlying progenitors and engines of the “haystack.”