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I am an astronomer with an interest in applying radio interferometers to the study of fast transients. Fast (i.e., subsecond duration) radio transients are generated by pulsars and stellar/exoplanet magnetospheres. At timescales faster than 1 second, propagation through plasma induces a measurable dispersion (frequency-dependent arrival time). Radio interferometers will be transformative because they simultaneously measure dispersion \emph{and} localize sources orders of magnitude better than traditional single-dish radio telescopes. That localization helps associate the radio emission with other objects, such as host galaxies or stars, to help us understand the transient and use it to probe the interstellar/intergalactic media.  The localization precision of a radio interferometer requires processing comes at the cost of managing  a torrential data stream. My work with the Very Large Array (VLA) has commissioned an observing mode that produces data rates of 1 TB hour$^{-1}$. I have written an extensive parallelized software system to search VLA data for transients. My collaborators and I have observed for 100 hours to produce 100 TB of data in the search for fast radio transients of various types. This new observing mode is pushing the VLA beyond its intended use and finding new, compelling science at those limits. The challenges of this effort are increasingly common in the sciences. My current efforts are focused on improving the parallelization and robustness of my radio transient search. More broadly, I am interested in developing the concept of \emph{real time anomaly detection} for massive data streams. In the study of radio transients, real-time detection would allow us to throttle the data stream by saving data only for the brief moments of interest. This process of "data triage" will be a key strategy to extracting science in data-intensive fields.