Professor Rachel Ward has been awarded a National Science Foundation (NSF) Transdisciplinary Research in Principles of Data Science, or TRIPODS, Phase II. The NSF initiative brings together scientists and engineers from different research communities to further the theoretical foundations of data science through integrated research and training activities. Ward is part of a team from the University of Texas at Austin that also includes Purna Sarkar, professor at the Department of Statistics and Data Sciences and Oden Institute affiliated faculty, Shuchi Chawla, professor at the Department of Computer Science and Sujay Sanghav, an associate professor at the Department of Electrical and Computer Engineering at UT's Cockrell School of Engineering.
Data science is becoming a key driver of innovation across virtually all sectors of society. It impacts how industry, government and academia operate day to day. But with ever-growing data sets, the complexities of accurately compiling and interpreting all this information is a challenge that requires the expertise of computer scientists, engineers, mathematicians and statisticians. Rachel Ward is a professor of mathematics at the College of Natural Sciences and core faculty member at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Her research lies broadly in the mathematics of data, with applications in signal and image processing, dynamical systems and biology.
Her work often synthesizes tools from optimization, numerical linear algebra, dynamical systems, scientific computing, sparse approximation, random matrix theory and machine learning.
“I’m honored to even be considered for this award,” Ward said. “The NSF are committed to leading the nation in foundational data science research and support for this kind of collaborative, interdisciplinary approach is a big step towards making that happen.”
TRIPODS is tied to NSF's Harnessing the Data Revolution Big Idea, which aims to accelerate discovery and innovation in data science algorithms, data infrastructure and education and workforce development.