The Scalable Algorithms for Data Science Laboratory (SCADS) conducts research at the intersection of algorithms, data analytics, and computational science and engineering. Our activities interact with a variety of areas including combinatorial scientific computing, network science, data mining and machine learning, and high-performance computing. Many of these areas do overlap with one another. Exploring their interface — with a focus on the synergetic application of novel combinatorial and numerical algorithms to develop scalable and robust methods for analysis and management of complex datasets — forms the core of our research goal. We invite you to browse through the Research page of this site for an overview of our current focus areas and projects (hovering over the Research tab will show you a list of the areas).
Our current research at SCADS is supported by NSF CAREER Award IIS-1553528 (2016–2021) and by the Voiland College of Engineering and Architecture/School of EECS (start-up fund). SCADS builds on prior research supported by various grants from NSF and the Department of Energy.