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The study of millisecond radio transients is important for a number of exciting problems in astrophysics, including the characterization of the intergalactic medium, discovering exoplanets, and understanding the lifecycle of the neutron star. These transients are rare and unpredictable, requiring extensive blind surveys for a chance to detect a single event. However, even a single detection can have huge science payoffs, since they can help understand exotic states of matter or illuminate distant corners of the universe.   Recent technological advances in radio astronomy, particularly the use of large arrays of antennas known as interferometers, enable data collection at time resolutions sufficient to study these phenomena with exquisite sensitivity, resolution, and flexibility. This power comes with the cost of handling data streams of 1 TB hour$^{-1}$. hour$^{-1}$, far faster than typical internet links can transport.  Next generation radio telescopes will increase this data flow and requisite computing requirements by orders of magnitude. Evolutionary changes to data analysis will not save radio astronomers from this data deluge; a deluge. A  revolutionary approach is needed. needed to find science in massive data streams.  I am interested in developing the concepts of \emph{real-time anomaly detection} and \emph{data triage} as solutions to this big data challenge.