REU Site: Data Intensive Scientific Computing

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REU Site: Data Intensive Scientific ComputingDouglas Thain and Kevin Lannon, University of Notre DameThis REU Site project will bring ten undergraduate students to the University of Notre Dame each summer for three years to perform research projects in scientific computing. These students will work in collaborative groups including experts in both high performance computing techniques and scientific fields that rely upon computing. Using advanced computational systems, the students will work in scientific areas such a gene sequencing, network visualization, particle physics, and astronomy. Students will gain experience in working in groups, using computational tools, and presenting scientific results, which will help to prepare them for entering a career in computational science.Computing has become an essential component of scientific discovery across the scientific disciplines. Novel algorithms combined with large scale infrastructure have the potential to multiply the power of simulation and data analysis tasks by many orders of magnitude. However, our current educational models are not aligned toward advancing scientific computing: computer science students have too little exposure to scientific applications, while physical science students have too little exposure to computing techniques. To address this, we propose an REU site in Data Intensive Scientific Computing (DISC) that will bring together undergraduates in computer science, physical sciences, and other interdisciplinary majors. Our objective is to train all participants in collaborative techniques so that they gain experience in both computing techniques and a physical science domain, as appropriate to their background. To achieve this, we have assembled a team of faculty from three distinct areas (computer science, physics, and biological sciences) that have already formed an active set of collaborations. Resources for the students and faculty involved will be provided by the Notre Dame Center for Research Computing (CRC) which provides large scale computing, storage, and communication facilities. Every participant will have access to a large shared cluster, a high performance parallel storage system and a data analysis cluster.