Data Intensive Sciences
Science of Large Data
Research is focused on pursuing theoretical developments and innovations to accommodate high dimensional data and very large-scale sets. These efforts are dedicated to discovering, evolving, and maturing analytic algorithms that efficiently scale to facilitate rapid analyses of massive data sets. The primary goal of this research area is to realize Army-relevant, high accuracy, predictive models based on massive data sets, which take advantage of emerging computing architectures. An additional area of research interest is maturation of methodologies to reduce data set dimensionality prior to modeling; thereby, greatly shortening computational time.
- Data Origination
- Scientific mathematics for large-scale data analytics
Computational Math for Data Analytics
Research is focused on identifying, evolving, and maturing innovative computational algorithms and methodologies to describe, model, simulate, solve, explore, and optimize control and coordination of computational systems impacted through physical events. Dynamic discrete event systems are data intensive and exist in many technological applications relevant to the Army from communications to system-of-systems to quantum sciences.
- Data analytics
- Reduced order models
Real-time Data Access & Analytics
Research is focused on exploration of new computing architectures, high-performance networks, and development of "middleware", software components that link high-level data analysis specifications with low-level distributed systems architectures. There is also a major interest in software targeted to end users, to support man-in-the-loop data analyses. Methodologies and techniques developed through this effort are expected to be highly beneficial in large-scale real-time or near real-time data accessing in many scientific disciplines. Army applications of interest include (i) live-virtual-constructive simulations and emulations for C4ISR; (ii) man-in-the-loop simulations for Army ground vehicles; (iii) training solders for cyber vulnerabilities; (iv) integrating computing and measurements for exploring new materials research; (v) cognition experiments feedback.
- Mathematical approaches for real-time analytics
- Computational methods for real-time large-scale data analytics