Mathematics of Data Science Summer School
In this two-week summer school for advanced undergraduate students, participants will be introduced to the mathematics underlying selected fundamental algorithms in data science, with a focus on methods of unsupervised learning for embedding and clustering data. Students will learn topics from high-dimensional probability, random matrix theory, graph theory, optimal transport, and spectral theory, among others. The material is geared toward students considering graduate study in the mathematics of data science.
Registration and Financial Support
Financial support in the form of reimbursement of travel and living expenses will be provided to facilitate students' participation. To apply, please submit your application materials at the link below. The application deadline is February 16, 2026.
Faculty hosts
Questions?
Email Professor William Leeb at [email protected].
