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Tucker to study large-scale social media data during Air Force fellowship

4/1/2015

UNIVERSITY PARK, Pa. — Conrad Tucker, assistant professor of engineering design and industrial engineering at Penn State, has been selected for a second consecutive year to participate in the U.S. Air Force Summer Faculty Fellowship Program (AF SFFP).

The program offers first-hand exposure to Air Force research challenges through eight- to 12-week residencies at participating Air Force facilities for full-time science, mathematics and engineering faculty in the U.S.

Tucker will spend eight weeks at the Wright-Patterson Air Force Base working with Kenneth Hopkinson, professor of computer science at the Air Force Institute of Technology (AFIT), on a proposal titled, “Capturing Energy Utilization Patterns by Mining Image Data Streams in Large Scale Social Media Networks.”

The work will be a continuation of the research that was completed last year as part of the AF SFFP.

Last summer, Tucker and Todd Bodnar, Penn State graduate student in biology, spent eight weeks collaborating with Hopkinson on a project involving smart grid and energy systems optimization by mining textual data within social media networks.

Tucker said, “Those eight weeks of research resulted in three papers and, perhaps more important, a solid partnership between the AFIT and Penn State. Hopkinson came to Penn State last fall and shared his expertise to help students and faculty gain a better understanding of the issues involved in the use of Internet technology in real-time applications.”

Tucker is in the process of establishing a collaborative effort between the AFIT and Penn State that centers on research and joint advising of Penn State engineering students.

In summer 2015, he will continue his research to provide insights into the ability of large-scale social media networks to predict energy utilization needs and trends.

Tucker said, “This project provides an interactive system that empowers the individuals using social networks and decision makers that leverage the power within them, to model and predict real-world events.”

His proposed methodologies for capturing, acquiring and mining large-scale social media data have the potential to solve problems in domains ranging from disaster response to healthcare and beyond.

“For example, we might be able to predict real, emerging strains on the energy system resulting from either natural (e.g., weather) or man-made (e.g., sporting) events,” explained Tucker.