I am a physics PhD student working in the
at the University of Washington with
on machine learning in astrophysics and cosmology.
I am a member of the Dark Energy Science Collaboration
and the Information and Statistics Science Collaboration
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
I was a
Duke Faculty Scholar
Prof. Kate Scholberg
in the Duke Neutrino and Cosmology Group.
Using detector simulations and Bayesian analysis, I developed data unfolding methods for the
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
In my free time I enjoy backpacking, skiing, rock climbing, and board games.
Developing a metric for evaluating observing strategies via the mutual information the observed
photometry contains about redshift.
A lower bound on the mutual information is estimated with a deep ensemble of normalizing flows
(built with my normalizing flow package, pzflow).
A python package for building normalizing flows to model the joint probability distribution of
Useful for forward modeling, data augmentation, and posterior calculation.
Using ensembles of broadband photometry to deconvolve high-resolution, photometrically calibrated
These deconvolved spectra achieve state of the art performance for template-based photo-z