KYLE SCHLUNS

Staff Data Scientist with 10+ years of experience developing, deploying, and scaling AI/ML systems across scientific, insurance, and cybersecurity domains. Proven track record transforming research algorithms into production-grade systems, architecting ML and data pipelines in cloud environments, and leading cross-functional technical initiatives. Early-career astrophysics researcher with peer-reviewed publications and hands-on telescope observing experience, including original astrometric measurements and analysis of large-scale astronomical survey data. Recognized for scientific rigor, technical leadership, and delivering innovative, high-impact solutions in fast-moving operational environments. Passionate about applying AI/ML to space science and complex real-world systems.

Peer-Reviewed Astrophysics Publications

Co-authored four scientific papers published in The Astrophysical Journal (ApJ) and The Astronomical Journal (AJ).

• Applied spectroscopic analysis to uncover dual supermassive black hole candidates, leveraging emission-line morphology and velocity offsets.
• Identified 100k wide low-mass binaries using photometric-distance matching and Monte Carlo chance-alignment validation.
• Contributed to a 70k-object M-dwarf spectroscopic catalog, measuring spectral features and magnetic-activity indicators across the Sloan Digital Sky Survey (SDSS).

Undergrad Thesis — Wide-Binary Astrometry & Stellar Magnetic Activity

• Conducted astrometric observations at the Perkins and Lowell Discovery telescopes to identify and validate wide-binary candidates through refined proper-motion measurements.
• Investigated magnetic-activity age indicators in 846 ultra-wide binaries, revealing coevality constraints, contamination patterns, and biases in activity fractions driven by Galactic height.