REU Site at Lamar University

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The Research Experience for Undergraduates (REU) program at Lamar University provides 8 students a 10-week authentic research experience in the Department of Mathematics during the summers of 2018-2020. Participants work in teams of four under the supervision of the PIs. An experienced research mentor and a junior faculty member lead each team. The REU site at LU aims to increase research opportunities for students at institutions where undergraduate research opportunities are limited or nonexistent. By recruiting underrepresented minorities, veterans and community college students, students are empowered to pursue degrees in STEM and to transition to advanced study or careers in STEM. The benefits of participating in a structured research program extend beyond the technical knowledge that students gain. Students increase writing and oral communication skills and are exposed to professional development opportunities in attending conferences to present their work. Furthermore, the faculty advisors continue to serve as mentors for the students beyond the ten weeks of the program. Each year, many students transfer to LU after having completed two-year degrees at local community colleges. The REU site at LU creates an opportunity for participants from local community colleges to meet other STEM students who successfully made the transition to a four-year university. Finally, pairing experienced research mentors with less experienced co-mentors on each research project gives the co-mentors the tools necessary to establish or expand undergraduate research programs. The intellectual focus of this program is on interdisciplinary research at the crossroads of mathematics, statistics and computer science. In many applications, dimension reduction is imperative for efficient manipulation of massive quantities of high-dimensional data. Dimension reduction using matrix factorizations and graph theory can be used to ascertain the underlying structure of the data and to find correlations among the data attributes. Interest is increasing in the study of graph theory, singular value decomposition (SVD) and nonnegative matrix factorization (NMF) due to applications in various research areas of mathematics and computer science. SVD and NMF are both unsupervised learning tools currently used in big data analytics. REU participants will gain exposure to the challenges and opportunities associated with big data. Students are introduced to the three Vs of big data: Volume, Variety, Velocity. Volume refers to the sheer quantity of the data, Variety refers to the different types of data, and Velocity refers to the speed of processing the data. Students are allotted the opportunity to participate in (1) theoretical research, with students investigating properties of graph theory, SVD and NMF to generate conjectures, prove theorems, and investigate existing algorithms; and (2) programming related research, with students performing experiments on matrix factorizations and graph theory and writing algorithms to process large data sets.