College of Idaho Stats Regional Statistics and Applied Mathematics Consulting Hub (Hub-REU)
- Institution
- The College of Idaho, ID
- Principal investigator
- Brandy S Wiegers
- Funding
- $447K
- Duration
- 2026–2029
Moonbase score
Still being scored
Abstract
The Regional Statistics and Applied Mathematics Consulting Hub Research Experience for Undergraduates (Hub-REU) site at the College of Idaho aims to advance quantitative research while supporting a pool of undergraduate students transitioning into the mathematical and statistical professions. By focusing on interdisciplinary partnerships between quantitative researchers and domain experts, the project demonstrates the real-world impact of statistical thinking and builds students' capacities to collaborate across scientific disciplines. Through collaborative research, students hone crucial professional skills, including cross-disciplinary communication, model building, project management, and joint problem-solving. Moreover, students engage in intentional professional development to build their problem-solving habits of mind, enhance their technical communication skills, and engage in professional development to prepare them for graduate study and STEM careers. Hosted over eight and a half weeks during the summer with follow-up post-REU conference participation, the Hub-REU annually engages eleven undergraduate students in collaborative applied mathematics and statistics research. The program introduces participants to a range of analysis techniques tailored to complex, real-world data, with research projects selected to match varying levels of mathematical and computational background. Potential research projects include modeling biological systems through compartmental differential equations, developing data analysis and dimensionality reduction pipelines for high-dimensional metabolomic data, and leveraging computer vision techniques for identifying unique ecological phenomena. Through these projects, students gain hands-on experience in mathematical modeling, multivariate statistics, data preprocessing, scientific visualization, machine learning, and reproducible research practices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Brandy S WiegersPI
- Eric Friedlander
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