Join us in Clemson, South Carolina, for a cutting-edge undergraduate research experience in data-intensive computing!
Data-intensive research is characterized by the need to efficiently acquire, store, transmit, manipulate, visualize, search, and analyze massive data sets. In recent years, investment in large-scale high-performance computing infrastructure has enabled an exciting opportunity to address "Big Data!" problems that are becoming increasingly common in nearly every area of science and technology. Co-funded by the Department of Defense in partnership with the National Science Foundation, our 2014 summer REU program has the following goals:
- Provide undergraduate research opportunities in wide range of data-intensive computing projects organized by an experienced team of faculty mentors.
- Provide training in valuable computational tools and techniques that will help students succeed in data-intensive research.
- Increase students' understanding of cutting-edge research in "Big Data" areas, as well as enthusiasm for continued research at the graduate level.
The program runs for 8 weeks from June 2, 2014 through July 25, 2014.
All students participating in the REU program will take part in tutorials on tools and techniques that are widely used in data-intensive computing research, as well as on useful professional development topics (e.g., applying to graduate school, writing NSF graduate fellowship applications). Weekly enrichment lectures will be provided by faculty mentors and visiting speakers to showcase the breadth of research opportunities available in the data-intensive computing domain. Undergraduate participants will have the opportunity to interact with incoming graduate students invovlved in a co-located NSF-funded program to help launch their research work. Numerous excursions, social events, and outings are also planned through the program. At the conclusion, students will present their work in a poster session, and funding is also available to help students to travel to regional and national conferences to present their work.
Students will be matched with a faculty mentor at the beginning of the REU program, and each student will participate in a focused research project. We have a large team of experienced faculty mentors working with this REU who supervise research in a broad range of data-intensive computing areas, from algorithms and data mining/analytics to high-performance compuing platforms to the software infrastructure required to support data-intensive applications. Research mentors include the following faculty:
|Amy Apon||Large-scale data analytics, high-performance computing|
|Brian Dean||Algorithms, optimization, data mining, medical informatics|
|Jill Gemmill||Cyberinfrastructure, computational science|
|Jason Hallstrom||Large-scale sensor networks, computational ecology|
|Feng Luo||Bioinformatics, biological databases|
|Brian Malloy||Software engineering, graphics and visualization|
|Jim Martin||Wireless networking, communication, mobile devices|
|John McGregor||Software engineering and maintenance|
|Ilya Safro||Computational science, data mining, network analysis|
|Murali Sitaraman||Reliable software engineering|
|Jacob Sorber||Mobile systems, sensor networks, pervasive computing|
|Pradip Srimani||Parallel and distributed computing|
|James Wang||Biological applications of data mining|
Here are just a few examples of some of the data-intensive research projects students might have the opportunitiy to join:
- Complex Systems Modeling (Amy Apon): Students will work on one of a couple of projects. In the first, we will develop models and experiments to measure performance and energy efficiency of exascale (really BIG!!) file systems. In the second, we will build predictive modes of how the presence of high-performance computing infrastructure impacts academic institutions.
- Medical Informatics (Brian Dean): Students will explore large-scale data-driven research in the domain of biological and medical informatics. For example, an ongoing collaboration with neurologists at the Medical University of South Carolina is investigating the use of advanced signal processing and machine learning algorithms for detecting signs of epilepsy in massive EEG (brain wave) datasets, and another project involves the use of network analysis algorithms to characterize neural connectivity patterns in autistic individuals.
- Intelligent River (Jason Hallstrom): The Intelligent River program is a campus-wide interdisciplinary initiative focused on safeguarding the planet's water resources through the development of new sensing, processing, storage, and visualization technologies. The project supports the development and deployment of a dense fabric of wireless sensors that will provide real-time access to essential environmental and hydrological parameters throughout the full 312-mile reach of the Savannah River. REU participants involved in the Intelligent River program will focus on the design, validation, and deployment of new embedded sensing platforms and supporting software systems. The experience will be anchored to a "hands-on" field experience within the Savannah River basin. Students will participate in instrumentation deployment, connectivity management, and empirical analysis.
- Next Generation Sequencing (Pradip Srimani, James Wang): Next generation sequencing technology is transforming the field of biology. Genome sequencing is the process of identifying the order of DNA nucleotides of an organism. Students will investigate sequencing large genomes using the Palmetto supercomputer.
Many other project opportunities exist, and you should feel welcome to contact the individual faculty members listed above if you have specific questions about projects they might be leading.
Application Details and Instructions
The REU program provides all student participants with low-cost on-campus housing (if needed), a generous stipend, library access, membership in on-campus recreational facilities, and assistance with travel costs to/from Clemson.
- Applicants must be a U.S. citizens or permanent residents.
- Applicants must be undergraduate in good standing at their home institions, with plans to complete their degree program.
- Students must be willing to work a minimum of 40 hours per week and take part in all REU activities (e.g., bi-weekly tutorials, enrichment lectures, excursions, poster sessions and presentations) in addition to their mentored research work.
To apply for the Clemson REU in data-intensive computing, please fill out the on-line application form available here. (this form is the "common REU application" developed at UNC-Charlotte for computing REU programs, so you may have encountered it with previous REU applications as well). In addition to this application, at least one recommendation is required from a faculty member at your institution. The faculty member should complete this form and email it directly to Dr. Brian Dean (email@example.com) with the subject line REU RECOMMENDATION.
Application review will begin in March, 2014. Applications will be accepted until all positions are filled.
If you have any questions about the details of this program, please feel welcome to contact the program director, Dr. Brian Dean (firstname.lastname@example.org).
About Clemson and its School of Computing
Located in the college town of Clemson in scenic upstate South Carolina near Lake Hartwell and the Blue Ridge Mountains, Clemson University is a public research university with a student population of approximately 17,000 students. Nearby cities include Anderson and Greenville, SC, and both Charlotte and Atlanta are about 2 hours away by car.The School of Computing at Clemson is home to several hundred undergraduate and gradute students and roughly 40 faculty in three divisions: computer science, visual computing, and human-centered computing. Thanks to recent investments in high-performance computing, Clemson computing researchers now have access to 15,000+-core supercomputer called the "Palmetto Cluster" running at nearly 100 teraflops, as well as a distributed "Condor" grid of 2600 CPUs across campus, and extensive resources for parallel computing on Graphics Processing Units (GPUs).