Many machine learning (ML) applications rely on having high-quality, labeled training data that is representative of the type of data the ML model will eventually be applied to. However, in many astronomical applications, we have the exact opposite, with observed training data that is substantially biased (often to the brightest, closest, best-measured objects) relative to the underlying populations of interest (the fainter, faraway, noisier objects). To account for these domain mismatch issues, astronomers often resort to various data augmentation strategies that include making the training data “noisier” and supplementing observed data with simulated data from theoretical models. While these broadly address the fundamental problems, they also tend to degrade the performance of the initial ML model. This project will explore new approaches to improve on these data augmentation strategies using state-of-the-art data from the DESI and SDSS-V astronomical surveys, with the goal of having a model that does strictly better on both observed (real) and simulated (theoretical) data under almost all circumstances. The main responsibilities of the student will be to review relevant literature, lead coding and data analysis efforts (using simulation studies and/or real data), and meet regularly with me and various collaborators to discuss progress on the project.