DST Groups

• Scientific Data Management (SDM), led by John Wu. The Scientific Data Management (SDM) group develops technologies and tools for efficient data access and storage management of massive scientific data sets. We are currently developing storage resource management tools, data querying technologies, in situ feature extraction algorithms, along with software platforms for exascale data. The group also works closely with application scientists to address their data processing challenges. These tools and application development activities are backed by active research efforts on novel algorithms for emerging hardware platforms. More details are also available at the SDM website
• Machine Learning and Analytics (MLA), led by Wes Bethel. The Machine Learning and Analytics (MLA) group is focused on enabling scientific researchers to achieve knowledge discovery goals through machine learning, visualization, and analysis. We develop new capabilities in high performance and data-intensive machine learning, visualization, analysis, and related (data-intensive) technologies. All our scientific collaborators have a theme in common: the need to understand complex systems through analytical or visual inspection of results from simulation and experiment/observation. More details are also available at DAV website
• Usable Software Systems (USS), led by Shreyas Cholia. The Usable Software Systems (USS) group is focused on usability aspects of computational and data analysis systems. We are involved in three primary research and development mission areas a) user-centered design processes that work in scientific environments b) usable scientific workflow tools and data abstractions and c) intuitive interfaces to explore, analyze, process data and run computations on HPC and distributed systems.
• Integrated Data Frameworks (IDF), led by Dan Gunter. The Integrated Data Frameworks (IDF) group performs research, development, and deployment of both research prototypes and 24/7 production services for analysis and dissemination of scientific data. We work with diverse scientific communities to provide data processing and analytics algorithms and pipelines for moderate-scale observation and simulation environments, optimization of data movement and management over next generation networking and data management solutions, and optimization and modeling of scientific workflows in integrated high-throughput simulation environments.