Research Overview

I am an observational cosmologist who uses the largest galaxy surveys to test our models of the Universe, and who builds the statistical and machine-learning methods that turn that data into physics. My work is anchored in the Dark Energy Spectroscopic Instrument (DESI) — where I am a builder and co-lead the Photo-z Topical Team — and in the Dark Energy Science Collaboration (DESC), where I co-lead the Lyman-Break Galaxies Topical Team.

My research falls into a few connected themes, from cross-correlating galaxies with the cosmic microwave background, to pushing cosmology into the high-redshift Universe, to the inference methods that decide what we can actually learn from data. Explore them below.

I am increasingly excited about bringing Machine Learning and Artificial Intelligence to bear on these problems — using these techniques to study our Universe with unprecedented precision. I am also drawn to the bridge between theory and observation: how do we actually know that we have learned something new about a model from data? If you are interested in collaborating, please reach out!

Galaxies × the Cosmic Microwave Background

Cross-correlating DESI's tens of millions of galaxies with maps of the cosmic microwave background to measure how structure grows — and to probe the σ₈ tension.

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The High-Redshift Universe with Lyman-Break Galaxies

Pushing cosmological analysis out to redshifts 2 < z < 5 with Lyman-Break Galaxies — and pinning down the systematics that stand in the way.

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Robust Inference & Machine Learning for Cosmology

Building the statistical and machine-learning machinery — model selection, emulators, systematics control — that decides what we can actually learn from cosmological data.

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The Milky Way & Early Research

From the Milky Way's giant γ-ray outflow to the rotation of newborn stars — Galactic and stellar astrophysics, including my earliest research.

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