Rate My Professor Charles Steinhardt

Charles Steinhardt is an Assistant Professor in the Department of Physics and Astronomy at the University of Missouri. He received an A.B. in Astrophysical Sciences from Princeton University, S.M. degrees in Astronomy and Computer Science from Harvard University, and a Ph.D. in Astronomy from Harvard University. Prior to his appointment at the University of Missouri, Steinhardt held the position of Associate Professor at the Cosmic Dawn Center, a Danish National Research Foundation Center of Excellence jointly hosted by the Niels Bohr Institute at the University of Copenhagen and the Technical University of Denmark, where he served as a founding member. He is a co-Principal Investigator of the BUFFALO survey, an initiative using Hubble Space Telescope observations of the six Frontier Fields clusters to study early galaxy assembly, clustering, and to identify targets for James Webb Space Telescope follow-up.

Steinhardt's research specializes in high-redshift astrophysics, galaxy evolution, astrostatistics, and machine learning, focusing on phenomena whose explanations are challenged by new theoretical developments or observations. His recent work proposes a new category of red star-forming galaxies that produce low-mass stars, appearing red while actively forming stars, which addresses discrepancies in black hole-to-stellar mass ratios and variations in the initial mass function between blue and red galaxies. Key publications include 'Do Red Galaxies Form More Stars than Blue Galaxies?' in The Astrophysical Journal (2025), 'Direct Evidence for Stellar Initial Mass Function Variation' (2026), 'The Impossibly Early Galaxy Problem' (2016), and 'Templates for Fitting Photometry of Ultra-High-Redshift Galaxies' (2022). Steinhardt directs an annual undergraduate research program, teaches courses such as ASTRON 1010 and an applied machine learning course, hosts the inaugural University of Missouri Physics Contest, co-organizes the 2025 Mid-American Regional Astrophysics Conference, and presents colloquia on topics including the universality of the stellar initial mass function and the falsifiability of inflation. He has also applied machine learning to Major League Baseball analytics, publishing on hitter and catcher adaptation.