Biography
Linda is a Physics graduate student specializing in cosmology and machine learning. Her research aims to bridge robust AI methodologies, high-performance computing, and fundamental scientific questions in astrophysics. In her early work, she developed physics-informed neural networks and field-level emulators for hydrodynamical simulations, including a differentiable end-to-end parameter inference pipeline. The applications of her work span the intergalactic medium, weak lensing, and cosmic microwave background (CMB) maps. She is currently working with observational data from Euclid and Rubin to robustly estimate high-redshift galaxy statistics as tracers of dark energy evolution.