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The newspaper of The Johns Hopkins University April 4, 2005 | Vol. 34 No. 28
SPSBE Pair to Look at Cell Phone Usage and Auto Accidents

By Holly Bodner

Hang Nguyen and Wen-Shuan Tseng, master's degree candidates in SPSBE's Department of Information Technology, are looking for an answer to a question everyone is wondering about these days: Is there a relationship between cell phone usage and auto accidents?

Recently selected as this year's GEICO Student Scholars in Discovery Informatics, the two recipients will use data mining techniques to explore the relationship between the two activities. The scholarship program is funded by the GEICO Philanthropic Foundation and coordinated by SPSBE's Department of Information Technology. GEICO, whose main business is automobile insurance, has a special interest in helping to identify accident trends to help make drivers and roads safer for the American public.

Nguyen, a resident of Gaithersburg, Md., earned a degree in economics from Hanoi Foreign Trade University and previously worked as a financial analyst for Vietnam Airlines. Tseng, of Baltimore, holds a degree in foreign languages from National Taiwan University and has conducted marketing and product development for telecommunications companies in Taiwan.

The students will conduct their research at the university's Montgomery County Campus, supervised by Jay Liebowitz, a professor, and William Agresti, an associate professor, both in the Department of Information Technology. "We felt that Hang and Wen-Shuan would be excellent for the program because of their academic interests and work experiences," Liebowitz said. "Wen-Shuan had also taken the data mining course, and Hang is currently taking it, which meant they could apply what they had learned to the project."

Data mining is the practice of analyzing large masses of data, looking for patterns and relationships. "It involves the use of very sophisticated computer analysis techniques to 'mine' for 'nuggets,' that is, interesting patterns in the data," Agresti said. When data mining is applied to traffic accidents, it may find combinations of factors that help to explain high accident rates.


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