Research
The research combines stellar dynamics, chemical abundances, large astronomical surveys, and machine learning to study the formation and evolution of the Milky Way.
Theme 01
How was the Milky Way assembled, and what can present-day stellar populations reveal about its past?
This direction uses stellar motions, orbital information, and population structure to distinguish different formation pathways and reconstruct the Galaxy's assembly history.
Theme 02
How do the disk, bar, bulge, and halo evolve together, and how is that evolution recorded in stellar orbits and chemistry?
This theme examines the dynamical processes that reshape Galactic structure and links them to the spatial and chemical organization of stellar populations.
Theme 03
What can large stellar spectroscopic surveys tell us about the chemical history of the Galaxy?
This direction focuses on extracting stellar parameters and elemental abundances from survey spectra and using those measurements to characterize Galactic populations and chemical evolution.
Theme 04
How can data-driven models recover useful physical information from the scale and complexity of modern astronomical surveys?
This theme develops machine-learning methods for classification, parameter estimation, and representation learning, with applications grounded in astronomical interpretation.