Summary
Andrew Horning is an Assistant Professor at RPI specializing in numerical analysis and scientific computing, where he builds fast, robust software for infinite-dimensional spectral problems that arise in fields from fluid stability to quantum scattering and network centrality. With a PhD from Cornell (2021) and prior teaching and applied-math instruction experience at MIT, he blends rigorous theoretical tools—numerical linear algebra, approximation theory, and operator spectral theory—with practical computational implementation. His recent work focuses on computational spectral theory and data-driven modeling for high-dimensional nonlinear dynamical systems, producing tools that bridge applied mathematics, physics, and data science. Based in Troy, NY, he brings seven years of research and teaching experience and a knack for turning abstract spectral problems into usable software for real-world scientific challenges.
8 years of coding experience