The National Science Foundation (NSF) awarded a 5-year, $241,269 Project Grant under the Computer and Information Science and Engineering (CISE) program to the Regents of the University of Michigan, Office of Research and Sponsored Projects. The grant supports research to close the gap between theory and practice in using randomization to design improved algorithms for ubiquitous matrix problems in data science. The project aims to: (1) reformulate optimal matrix sketching via black-box sampling methods; (2) develop randomized iterative refinement algorithms via stochastic optimization; and (3) study the robustness of randomized numerical linear algebra algorithms to preserve structural elements of data. The research seeks to provide the algorithmic foundations necessary to enable broader practical adoption of randomized linear algebra algorithms across computational data science applications. No subcontract or subaward details were provided.