Professor Bae's research focuses on the physical understanding and modeling of structures associated with near-wall turbulence. Her main research goal is to develop high-fidelity models that reduce the computational cost to simulate high-Reynolds-number turbulent flows. These models will allow simulations to be utilized in the design cycle of wind farms and aircrafts and in predictions of atmospheric flows, reducing the overall time and effort associated with these processes. She also studies the physical mechanisms that generate and sustain turbulence, which, in turn, fuels new modeling approaches. She has interests in applying machine learning, information theory, and other novel methods to turbulence modeling.