Chase Enlowsmith
I am a post-baccalaureate researcher holding Bachelor of Science degrees in Physics and Astronomy from the University of Texas at Austin. My research interests lie at the intersection of computational astrophysics and early-universe cosmology. I leverage accelerated computational tools and machine learning to probe the cosmic microwave background, large-scale structure, and inflationary theory. Parallel to my astrophysical research, I am deeply invested in foundational artificial intelligence, interpretable AI, and agentic workflows. I actively apply these methodologies to scientific machine learning, specifically focusing on Einstein-Boltzman code emulation and deriving analytic models for galaxy cluster mass bias.
I am also passionate about making theoretical physics accessible through open-source pedagogical software, most notably through the development of CMBverse, an interactive computational tool for physics education.
Looking forward, I aim to translate my computational frameworks toward more fundamental theoretical domains. I am highly interested in the architecture of foundational AI systems, as well as problems in high-energy theory, quantum field theory, and general relativity.
Outside of my research, I dedicate my energy to backpacking, trail running, skateboarding, and music.
