I am a physics PhD student working in the DIRAC Institute at the University of Washington with Prof. Andy Connolly on machine learning in astrophysics and cosmology. I am a member of the Dark Energy Science Collaboration (DESC) and the Information and Statistics Science Collaboration (ISSC) of the Vera C. Rubin Observatory. My research focuses on photometric redshifts for cosmology and galaxy evolution, including forward modeling systematic errors and their impact on cosmology analyses, using the Rubin Observatory to measure the Lyman-alpha Forest and map the intergalactic medium, and using deep learning to accelerate the Rubin Observatory active optics system. I use a variety of machine learning tools, including normalizing flows, convolutional neural networks, and variational autoencoders.

I received a bachelor's in physics from Duke University, where I graduated summa cum laude with highest distinction. I was a Duke Faculty Scholar working with Prof. Kate Scholberg in the Duke Neutrino and Cosmology Group. Using detector simulations and Bayesian analysis, I developed data unfolding methods for the HALO supernova neutrino detector. I also spent a summer at the Karlsruhe Institute of Technology, working with Dr. Andreas Haungs to characterize the muon content of cosmic ray air showers detected in the IceTop array.

In my free time I enjoy backpacking, skiing, rock climbing, and board games.

Recent Work