I am a physics PhD student at the University of Washington working with Prof. Andy Connolly on machine learning in astrophysics and cosmology. I am a member of the DIRAC Institute and the Dark Energy Science Collaboration (DESC) of the Vera C. Rubin Observatory. My current focus is on photometric redshift estimation (photo-z's), forward modeling of photo-z errors, and the deconvolution of spectra and light curves from broadband photometry. I use a variety of machine learning tools, such as normalizing flows, variational autoencoders, Gaussian processes, and more.
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.