Data Science & Technology
The Data Science and Technology (DST) Department delivers leading-edge, innovative methods for solving data-intensive science problems. DST activities range from basic and applied research to deployment of software tools. Our projects span a diverse set of activities, including data management; data movement; cybersecurity; machine learning, statistical, topological, and geometric analysis/analytics; computer vision; visualization; user-interface design; usability; end-to-end data-intensive system architecture and deployment. We focus on conceiving, developing, and applying leading-edge, innovative methods for solving data-intensive science problems.
Department Head: Deborah Agarwal
The Machine Learning & Analytics Group conceives, designs, and implements new methods in high-performance machine learning, data and image analytics, computational geometry and topology, and visualization technologies. Our work involves a mix of theoretical research and applied research. The group works in close collaboration with scientific partners to identify and address challenging, large-scale, data-rich problems emerging from simulations, experiments, and observations. Read More »
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. Read More »
The User-Centered Systems Group is focused on usability aspects of computational and data analysis systems. UCS researchers are involved in three primary research and development mission areas: 1) User-centered design processes that work in scientific environments; 2) Usable scientific workflow tools and data abstractions, and; 3) Intuitive interfaces to explore, analyze, process data and run computations on HPC and distributed systems. Read More »
The Integrated Data Frameworks (IDF) group works in a wide variety of scientific domains, building tools and models to allow scientists to move from the “raw” data generated by simulations, experiments, and observations, to a combined quality-controlled view of the data that they can easily manipulate to gain scientific insight. Often this process will involve extensive work on “cleaning” and transforming data in custom data pipelines, and often there will also be a strong user interface component to either the pipelines or the final data products. Read More »