REU Site: The University of Texas Rio Grande Valley REU Program on Applied Mathematics and Computational and Data Science

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The Research Experiences for Undergraduates (REU) site at The University of Texas Rio Grande Valley, will host eight students over the course of nine weeks to work collaboratively on group research projects in applied mathematics, mathematical modeling and computational and data science, applying theoretical models to physical and biological phenomena. Students will acquire a wide range of computational skills by receiving training on numerical methods and their coding. The program will provide students with invaluable educational and research skills and experiences and better prepare them for graduate programs and careers in STEM. One of the goals is to diversify the STEM fields, with plans for more than 50% of participants coming from under-represented minorities and under-represented groups in STEM. The program will also provide mentoring to early-career faculty, allowing them to grow professionally as effective mentors for undergraduate research themselves. Students will disseminate research results by presenting at mathematics and interdisciplinary conferences, publishing papers in journals, and by coding computer software, which will be made freely available. This REU program will provide undergraduate students with vigorous, motivating, and above all quality collaborative research experience in the areas of stochastic and deterministic partial differential equations and numerical analysis with an emphasis on the computational aspects. Additionally, students will develop and/or apply deep spatial learning algorithms to spatial processes resulting from partial differential equations. As a first objective, students will study and analyze novel and engaging topics such as the dynamics of scattering waves and propagation of nonlinear waves in non-uniform media. As a second objective, students will work on simulating spatial processes such as the spread of diseases or emission of pollution, which are modeled using partial differential equations with space-time white or color noise. Students will also produce predictive models and compare their outcomes to actual models of spatial processes. 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 applicants to graduate programs as well as in industry. Moreover, students will be enriched academically with workshops in scientific writing, presentation skills, and graduate school readiness.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.