  Jets are suppressed and modified in heavy ion collisions, which serve as
powerful probes to the properties of the quark-gluon plasma (QGP). Attributed
to the abundant information carried by the jet constituents and reconstructed
substructures, plenty of interesting applications of machine learning
techniques have been made on a jet-by-jet basis to study the jet quenching
phenomena. Here we review recent proceedings on this topic including the tasks
of reconstructing jet momentum in heavy ion collisions, classifying quenched
jets and unquenched jets, identifying jet energy loss, locating the jet
creation points as well as distinguishing between quark- and gluon-initiated
jets in the QGP. Such jet-by-jet analyses will allow us to have a better handle
on the jet reconstruction and selections to investigate the effects of jet
modifications and push forward the long-standing goal of jet tomographic probes
of the QGP.
