Time domain astronomy, or the study of the dynamic universe on human timescales, stands at the forefront of a revolution fueled by the advent of large surveys. We have recently experienced an unprecedented influx of observations that led to the discovery of exotic transients such as superluminous supernovae or tidal disruption events. The upcoming deployment of next-generation survey telescopes, such as the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, will increase our transient detection capabilities by two orders of magnitude.

Developing machine learning techniques will prove to be not only useful, but necessary, to deal with the deluge of data we will obtain from these observatories, promising deeper insights into known cosmic phenomena and the exciting prospect of discovering entirely new classes of transients.