In this talk, I explore the impact of stellar kinematics on understanding the particle nature of Dark Matter in four separate locations: the solar neighborhood, the Galactic center, dwarf galaxies, and streams. I first discuss the implications of the different stellar components on direct detection experiments. I show how to use the velocity distribution of stars in the solar neighborhood to determine the empirical velocity distribution of Dark Matter, critical for direct detection. I then use an example of a Dark Matter candidate annihilating to gamma rays in the Galactic Center to motivate the need for accurate measurements of the Dark Matter density profile. Subsequently, I motivate the need for better understanding of the density profiles of dwarf galaxies given their large implications on indirect detection, by analyzing mock data and reconstructing the inner slope of the Dark Matter profile. Finally, I close by arguing for the importance of streams in identifying the Dark Matter subhalo population, and present a new stream finding algorithm called Via Machinae based on unsupervised machine learning techniques.