Faculty and Student Teams Program

Methods for Analysis of Extreme Scale Data and Extreme Scale Computing Systems

Requesting applications from science or engineering faculty members at institutions serving students underrepresented in science, engineering, mathematics and technology to work on the following projects at Oak Ridge National Laboratory (ORNL).

Project Description

Develop mathematical, statistical, and computational methods that enable diverse analysis capabilities for extreme scale data sets. Another area is to develop anomaly analysis and predictive methods for events, such as hardware and software faults, in a high-performance computing environment. Specific analysis methods and application data that are of mutual interest to the project, the faculty, and the students will be determined at the start of the project.

George Ostrouchov ostrouchovg@ornl.gov

Applicants’ Responsibilities and Relationship to Project

Applicants will receive support under the Department of Energy Faculty Student Team Research Program (FaST) to work collaboratively with the project research team at the Laboratory for up to 10 weeks during the summer of 2010. The exact appointment period in the time frame of June to August will be scheduled by mutual agreement between the host divisions at Oak Ridge National Laboratory and the successful applicant.  Faculty will be expected to identify two to three students from their campuses to participate in the FaST program. The faculty member will provide some mentorship to students during the summer research activities. The faculty and students must participate as a group and serve their appointments concurrently.  It is expected that the faculty member and the students become an integral part of the research team working on this project and that opportunities for continued collaboration may be identified.

Qualifications of Ideal Candidate

The ideal candidate will have a PhD or equivalent background in Computer Science, Mathematics, Statistics, or another related field as well as experience in developing and implementing data analysis methodology. Experience with the development of parallel algorithms in a high-performance computing environment is desirable but not required.

Support and Financial Commitments

The successful candidate will receive a stipend based on the academic salary, travel expenses to and from the Laboratory, and a housing allowance. Students recommended by the faculty member for participation in the program will receive a stipend of $425/week for each week at the Laboratory, plus a housing allowance, and reimbursement for transportation expenses to and from the ORNL.  Funds are provided for this program from the US Department of Energy, Office of Science in partnership with the National Science Foundation, from ORNL, and from other sources.

See Financial Information.