May 31st – August 9th
A completed application includes:
- A three-page SU REU application form, which can be downloaded online:http://faculty.salisbury.edu/~ealu/REU/ApplicationForm.docx
- An online CISE REU Common Application
- A transcript from your current institution. While an official transcript is preferred, an unofficial one is acceptable. An official transcript is required when you are selected as the REU participant finalist.
- Letters of recommendation from two faculty who have worked with the applicant. The recommendation letter form can be downloaded online: http://faculty.salisbury.edu/~ealu/REU/RecommendationForm.docx. The recommendation letters should be sent by the faculty directly to us in either of email, mail or fax.
- A proof of U.S. citizenship (passport, birth certificate, or similar) or permanent residency (“green card”) is required when you are selected as the REU participant finalist. Please understand that U.S. citizenship/permanent residency is a requirement for the NSF REU funding and we cannot change or waive it.
United States citizens or permanent residents
A STEM (Science, Technology, Engineering, and Mathematics) major
Completing at least sophomore year of study
GPA 3.0 or above
Programming knowledge in either of C, C++, Java, or MATLAB
Ten-week summer undergraduate research program
$5,000 stipends and $600 travel allowance
On-campus housing and meal allowance provided
Field trips and social activities
Research opportunities in emerging computing with applications in science and engineering
Experienced faculty mentors from Computer Science, Mathematics, Computer Engineering, GIS, and Electrical Engineering
We are excited to host this unique Research Experiences for Undergraduates (REU) Site: EXERCISE – Explore Emerging Computing in Science and Engineering. It is the first REU site at Salisbury University and the first CISE REU site in the Eastern Shore of Maryland.
EXERCISE (Explore Emerging Computing in Science and Engineering) is an interdisciplinary project that explores emerging paradigms in parallel computing with data and compute-intensive applications in science and engineering.
In the EXERCISE project, students will apply emerging parallel computing models including GPU computing with NVIDIA CUDA (a local parallel processing system) and MapReduce computing on Amazon EC2 (a distributed parallel processing system) to tackle data and compute-intensive problems in computer networks and security, image and signal processing, and geographic information system.