  Ultracool dwarf stars and brown dwarfs provide a unique probe of large-scale
Galactic structure and evolution; however, until recently spectroscopic samples
of sufficient size, depth, and fidelity have been unavailable. Here, we present
the identification of 164 M7--T9 ultracool dwarfs in 0.6~deg$^2$ of deep,
low-resolution, near-infrared spectroscopic data obtained with the Hubble Space
Telescope Wide Field Camera 3 instrument as part of the WFC3 Infrared
Spectroscopic Parallel Survey and the 3D-HST survey. We describe the
methodology by which we isolate ultracool dwarf candidates from over 200,000
spectra, and show that selection by machine learning classification is superior
to spectral index-based methods in terms of completeness and contamination. We
use the spectra to accurately determine classifications and spectrophotometric
distances, the latter reaching to ~2 kpc for L dwarfs and ~400pc for T dwarfs.
