Algebraic Geometry: A Window to Machine Learning
Funding supports attendance at a workshop uniting algebraic geometry and machine learning to develop new mathematical frameworks for analyzing and advancing deep learning systems.
Funder: Institute for Pure and Applied Mathematics
Due Dates: December 9, 2026 (Applications for fullest funding consideration)
Funding Amounts: Support for travel, accommodation, and registration; priority for early-career researchers and students
Summary: Workshop funding to foster research at the intersection of algebraic geometry and machine learning.
Key Information: Indicate funding request on registration form; in-person spots may be limited.
Description
This opportunity provides funding to attend the workshop "Algebraic Geometry: A Window to Machine Learning," held February 1–5, 2027 at IPAM (UCLA). The workshop unites mathematicians and scientists to develop new mathematical frameworks—rooted in algebraic geometry—for analyzing and advancing modern machine learning systems. Key topics include the algebraic and geometric structure of neural networks, feature learning, neural collapse, overparameterization, and other open problems in deep learning. The event aims to catalyze cross-disciplinary research and foster collaboration between pure mathematicians and learning theorists.
