Recent burgeoning of processing speed and sources of data have created a tremendous demand for mathematically-founded approaches to modeling data for exploration, understanding, and prediction. Relevant mathematical tools span the full discipline, from algebraic geometry used for existence and uniqueness proofs of low rank approximations for tensor data, to category theory used for natural language processing applications, to approximation and optimization frameworks developed for convergence and robustness guarantees for deep neural networks. These research problems are inherently interdisciplinary, requiring mathematics, computer science, and data domain expertise. Many of these problems also have immediate applications in industry and government.

This one-week workshop consists primarily of time spent in small research groups actively working to solve problems in data science. Participants typically range from senior researchers to early graduate students, collaborating as equals and building relationships centered on shared research interests and complementary research skills. Our previous two workshops have produced follow-on sessions at national mathematics conferences and two volumes of early research results in the AWM-Springer series (2017 and 2019).

*You will be asked to submit your ranked preference of projects in the application. The project descriptions can be found here: http://www.ipam.ucla.edu/programs/special-events-and-conferences/women-in-data-science-and-mathematics/?tab=projects

**Graduate students only: your application is not complete without a letter of reference. Please add your reference to the Mathprograms.org “Standard Coversheet” and select the reference in the application.

IPAM is committed to equity and diversity in the mathematical sciences. We strongly encourage members of traditionally underrepresented groups including women to apply.