REU Site: UT Rio Grande Valley REU Program on Applied Mathematics and Computational and Data Science (AMCADS)

About This Grant

The University of Texas Rio Grande Valley REU Program in Applied Mathematics and Computational and Data Science (AMCADS) will engage eight talented students in mathematics each summer for a nine-week immersive research experience. Students will work collaboratively in teams under the direction of the senior researchers on mathematical problems with real-life applications in biology, physics and health sciences. Students will learn how to use MATLAB and Python programs; understanding how to address each model computationally will have a broad impact on the students' ability to tackle other mathematical models and be competitive as graduate applicants as well as in industry. Students will be also enriched academically with workshops in scientific writing, presentation skills, and graduate school readiness. One of the project's main objectives is to encourage participants to consider graduate programs in mathematics and data sciences and to help them discover which area of research interests them most. The project will thus strengthen the U.S. scientific workforce. The students and the PIs will disseminate results through conferences, publishing papers, and by coding programs that will be made freely available. Specifically, the student researchers will study and analyze novel and engaging topics such as numerical simulations for fractional and stochastic partial differential equations (PDEs), modeling the spread of diseases, modeling the accumulation of toxins causing Alzheimer's disease, and modeling collective group behavior. As a second objective, solutions of the PDEs will be rendered as the data used for training and testing the neural networks. At the end, students will learn how to apply the produced neural networks to actual data. Students will also produce predictive models and compare their outcomes to simulations of the actual models of the dynamical processes. 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.