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Georgia Tech to Analyze Massive Data Sets Using Visual Analytics

Enormous amounts of data are being generated in health care, computational biology, homeland security and other areas, but analyzing these massive and unstructured data sets has proven cumbersome and difficult. An emerging research field known as data and visual analytics is helping sift through such mountains of information to find and put together individual pieces of a picture.

The Georgia Institute of Technology has received a five-year grant to lead and coordinate a new initiative that will develop foundational research in massive data analysis and visual analytics. A research team headed by Haesun Park, a professor and associate chair in the Computational Science and Engineering Division of the Georgia Tech College of Computing, will investigate ways to improve the visual analytics of massive data sets through machine learning, numerical algorithms and optimization, computational statistics, and information visualization.

“Developing new and improved mathematical and computational methodologies will further enable systems developers, intelligence analysts, biologists and health care workers to implement new methods to ‘detect the expected and discover the unexpected’ among massive data sets,” Park explained.

The $3 million joint National Science Foundation and Department of Homeland Security grant establishes Georgia Tech as the lead academic research institution for all national Foundations of Data and Visual Analytics (FODAVA) research efforts. Seven other FODAVA Partnership Awards will be announced later this year, all working in conjunction with eleven Georgia Tech investigators to advance the field.

Over the next five years, the Georgia Tech-led research team will work to establish FODAVA as a distinct research field and build a community of top-quality researchers that will collaborate on research workshops and conferences, industry engagement and technology transfer.

 From law enforcement and intelligence gathering to electronic heath records and computational biology, the accurate and timely analysis of massive amounts of information is critical to deeper understanding and effective decision making.

“Collaborations across Georgia Tech’s computing, engineering and mathematics disciplines aim to develop better scientific and foundational methods to help practitioners in many different lines of work analyze and interactively explore large data sets more efficiently and effectively,” Park added.

Source: Georgia Institute of Technology