Generating nuanced yet interpretable hypotheses from noisy and high-dimensional observations is a core activity across numerous scientific disciplines. For example, across neuroscience, genomics, biomechanics, and ecology, researchers analyze images and videos to study phenomena ranging from the genetic expression of lab mice to global species distributions of birds. Useful scientific hypotheses are typically instantiated on symbolically interpretable concepts and attributes (such as stride periods and center of mass oscillations when studying kinematics of walking). A key challenge is to extract such symbolically interpretable hypotheses from raw high-dimensional observational data. To address this challenge, this project aims to develop a novel neurosymbolic programming framework, called foundation model programming, for generating symbolically interpretable scientific hypotheses from high dimensional observational data. The main idea is to represent interpretable hypotheses as neurosymbolic programs that use symbolic primitives as well as neural modules, including foundation models. The use of neural and foundation models allows the hypotheses to be full-stack, modeling both the extraction of relevant patterns and motifs from high-dimensional raw data and reasoning over those patterns. The core technical benefit of this approach is that it inherits both the contextual flexibility of modern foundation models given high-dimensional inputs, and the rigorous reasoning a