AstroAI Workshop 2026
Drew Oldag
Hyrax - A low-code solution for rapid experimentation with machine learning and unsupervised discovery in astronomy.
Presenter: Drew Oldag (University of Washington)
Title: Hyrax - A low-code solution for rapid experimentation with machine learning and unsupervised discovery in astronomy.
Date/Time: Thursday, June 18, 11:30 AM - 12:30 PM
Abstract: With current and upcoming large astronomical surveys producing data at unprecedented scale, the limiting factor for ML-driven discovery is increasingly not the data itself, but the infrastructure required to work with it. Astronomers routinely spend a significant amount of their time on data wrangling, configuration management, and bespoke pipeline engineering — effort that comes directly at the expense of science; and is often not reusable by other research groups/teams resulting in duplicated effort.
Hyrax is an extensible GPU-enabled framework that provides infrastructure for the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive 2D/3D latent-space exploration for unsupervised discovery.