I am a PhD student at the University of Washington working on problems in cosmology and astronomical instrumentation, with a focus on using machine learning and forward modeling to extract physical insight from complex data. My current work spans two threads: developing deep-learning–based optical system diagnostics for the Vera C. Rubin Observatory, where I use convolutional networks and domain adaptation to infer telescope aberrations in real time; and building fast, physically informed emulators to model the impact of reionization and baryon–dark matter streaming on the small-scale structure of the universe. I’m broadly interested in methods that connect simulations and observations and finding optimal ways to deal with large volumes of data using machine learning.

I am fascinated by dark matter and my research is focused around constraining its nature. I am particularly excited about using stellar streams as a tool to probe the properties of the smallest subhalos.

Beyond research, I enjoy reading, hiking, skiing, coffee shop exploring, and photography.

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.