NSF OCI REU Site 2013:
Summer Research for Undergraduates in High Performance Data Mining
The Department of Computer Science at Georgia State University hosts the NSF OCI REU Site: Summer Research for Undergraduates in High Performance Data Mining. The 8-week NSF OCI REU summer research program runs from 5/30/2013 (Thursday) to 7/24/2013 (Wednesday). 10 students' research projects focus on high performance data mining techniques merging parallel computing, cloud computing and machine learning and their broad applications such as bioinformatics and security. Students will do research master professional research skills under professional guidance by faculty mentors and industrial mentors. A student will have a stipend ($4,000) plus a financial support ($2,300 for housing and meal, and $600 for travel). The NSF OCI REU summer research program will be beneficial for underrepresented students (especially minority and women).
Graduate students will help REU students do joint research projects. The REU students will submit research papers for real publications. They will participate in student data mining competitions such as the Data Mining Cup and UC San Diego Data Mining Contest. The 10 REU students will visit Georgia Tech Communications Assurance and Performance Group to learn latest network security techniques. Various research activities and social activities will be organized to enrich the REU students' educational and research experiences, and to strengthen REU student-GSU-Georgia Tech faculty mentor interaction, REU student-industrial mentor interaction, and REU student-GSU-Georgia Tech student interaction.
The web-based student data mining club (a global student data mining e-community) will be used by not only the REU students but also other students who are interested in high performance data mining research and its applications. The REU students' research projects and experiences will be posted on the NSF REU website to encourage nation-wide undergraduate students to actively do research to become outstanding future workforce in high performance data mining.