Analyzing enormous amounts of data generated daily is essential to progress in all research areas. To achieve advances in critical areas of science and technology, Georgia Tech is leveraging its unique expertise in “Big Data” to provide solutions that will transform the ability of individuals and organizations to analyze large and complex sets of data.
For example, the use of Big Data techniques to better understand social networks could help tackle challenges such as understanding critical trends in behaviors and customs, and influencing change. These computational capabilities could also be applied to even more taxing issues such as finding vulnerabilities in the power grid and monitoring important protein interactions in cancer research.
Through a number of campus units, including the Institute for Data and High Performance Computing (IDH) and the Georgia Tech Research Institute (GTRI), Georgia Tech supports multidisciplinary research teams that are both developing innovations in computational methods to advance Big Data analysis, and applying these techniques to industry, business, and the public sector. Enabling technologies under development include data visualization, advanced analytics, machine learning, and high-performance computing. Application areas for Georgia Tech’s Big Data research agenda include astrophysics, biomedicine, combustion, energy, finance, healthcare, manufacturing, materials, information and cyber security, social networks, sustainability, and transportation. Both undergraduate and graduate students contribute to research in these critical areas.
Mark Richards, David Bader and Dan Campbell
(left-to-right) pose in the Advanced Computing
Technology Lab operated by the Georgia Tech Research
Institute. (Credit: Gary Meek) Full Story >
As the lead institution for the Foundations on Data Analysis and Visual Analytics (FODAVA) research initiative, Georgia Tech performs foundational research in massive data analysis and visual analytics. Researchers investigate ways to improve the visual analytics of massive data sets through advances in areas such as machine learning, numeric and geometric computing, optimization, computational statistics, and information visualization.
Machine learning, an important part of the Big Data environment, is a critical focus at Georgia Tech, where researchers are leading efforts to build and disseminate scalable machine learning software. Machine learning methods find patterns in data that people may have difficulty identifying, and these methods have applications in virtually every discipline and human enterprise. Georgia Tech also provides technical leadership for the national Center for Adaptive Supercomputing Software for Multithreaded Architectures (CASS-MT) and directs efforts within the center to develop methods for analyzing massive and complex semantic networks.