Caltech/MIT's LIGO, the largest gravitational-wave observatory in the world, collects data on minute space-time ripples from cataclysmic astronomical events like colliding black holes or supernovae. Classifying LIDO's data as either an event of interest or an unknown "glitch" with high accuracy poses a challenge, due to the volume of highly complex data collected by the observatory. A recent dissertation by Columbia University's Robert Colgan [1] proposes a neural network to accurately separate non-astrophysical glitches, achieving significantly higher classification accuracy than previous methods. Gravitational waves, first proposed by Einstein in his general theory of relativity, are caused by massive objects -- like black holes--curving spacetime. The waves ripple through the universe at the speed of light, distorting space and time as they compress and stretch distances.