This research combines galaxy simulations with machine learning to study the invisible gas surrounding galaxies. By training a neural network to interpret astronomical observations, the project creates a public tool—the Circumgalactic Dictionary—that enables previously impossible measurements, advancing our understanding of galaxy evolution and the origins of stars, planets, and life.

This research investigates white dwarfs and the planetary debris that surrounds them. By developing a technique to detect transiting debris systems, the researcher has expanded the known population of these rare objects, helping astronomers understand how planetary systems evolve, survive, and ultimately break apart after their host stars die.

My thesis describes how laboratory experiments recreate nuclear reactions occurring on accreting neutron stars. By developing a novel particle detection system, I achieved the first simultaneous neutron–proton measurements, enabling more complex studies that illuminate extreme matter, stellar evolution, and the cosmic origins of elements fundamental to life.