NSF REU Big Data

The North Dakota State University (NDSU) Departments of Computer Science and Industrial, Manufacturing Engineering and North Carolina A&T State University department of Industrial and Systems Engineering are pleased to offer a special research experience for undergraduate students in big data analytics and machine learning with an international experience component in Chile. Apply Here.

About the Program

The National Science Foundation (NSF) funds a large number of research opportunities for undergraduate students through its REU Sites program. An REU Site consists of a group of ten or so undergraduates who work in the research programs of the host institution. Each student is associated with a specific research project, where he/she works closely with the faculty and other researchers. Students are granted stipends and, in many cases, assistance with housing and travel. Undergraduate students supported with NSF funds must be citizens or permanent residents of the United States or its possessions. An REU Site may be at either a US or foreign location (https://www.nsf.gov/crssprgm/reu/).

Goals

The goals of the program are to:

1. Recruit undergraduate students each year from STEM disciplines
2. Engage participants in a 10-week long high quality multi-disciplinary research experiences
3. Provide effective mentoring to participating students through various mechanisms including organizing mentoring sessions, professional events, and company visits
4. Establish post-REU mentoring mechanisms to continue mentoring through the academic year and organize virtual research poster symposium in the following spring semester
5. Provide participants with a 4-week long international research experience in real world big data and machine learning at the University of Chile, a reputed research institution in South America

Participants

Selected participants will get involved in research related to big data analytics and machine learning. Participants will also gain an appreciation of research field via software development and engineering activities and field trips.

Participants will receive a stipend (up to $6,000), room and board, funding for travel to and from NDSU (up to $700) as well as to and from NDSU to Santiago, Chile (up to $1,500).

The summer 2022 REU program will run from May 31 until August 5, 2022. Application review will begin form February 15, 2022 and continue until all positions have been filled.

Participating undergraduates must be citizens or permanent residents of the United States who are enrolled in undergraduate degree programs. Women and members of traditionally underrepresented groups are particularly encouraged to apply. In addition, all participants must possess a valid passport for the travel to Chile.

Planned Activities

Multidisciplinary research projects: Potential research projects are provided on this website for your information. Participants have the unique opportunity to work in an interdisciplinary environment with highly talented scientific mentors. Most experimental projects involve hands-on research in one or more of the state-of-the-art central instrumentation facilities with training and supervision provided by expert technical staff.

Faculty-Led Seminars: Faculty and researchers involved in this program will present various research topics in this program and discuss future opportunities.

Workshops and seminars: Short workshops and seminars will be offered for developing professional skills needed in industry, research, and graduate school.

Networking & Social Activities: Students will have opportunities to explore the sites and summer activities in the Fargo-Moorhead area. Learn more about Fargo.

End-of-Program REU poster symposium: At the end of the REU program, students will have the opportunity to formally present their research experiences through an REU poster symposium.

Possible Research Projects

• Predictive modeling of production, consumption, and demand analysis for various energy systems
• Quantifying strength degradations resulting from cyclic loading in fine-grained soils using machine learning
• Assessment of consumer purchasing and education of beef quality attributes using mobile applications (Android and iPhone operating system: IOS)
• Data-driven prognostics and health management framework for additive manufacturing processes
• Reliability assessment of complex infrastructure networks using big data and convolutional neural networks