Daniel Gause

Daniel Gause is a data scientist and aspiring astrophysicist with a background in physics and mathematics from Middlebury College. As an undergraduate, he researched high-redshift dual quasars, sparking a lasting interest in galaxy evolution. He has since spent four years building machine learning models and data pipelines across healthcare, defense, and science.

His work focuses on applying machine learning to astrophysical problems, including quasar classification, survival modeling, and clustering large-scale datasets. He’s also exploring remote sensing and ecological modeling to study biodiversity and planetary systems. Across fields, he looks for elegant ways to extract insight from complex data.

Dan is currently a Research Associate at the California Academy of Sciences, using hyperspectral data to map gaps in global plant collections. He also works on adapting LSST pipelines for the Nickel Telescope at Lick Observatory. He’s driven by curiosity, clean code, and a passion for interdisciplinary science.