NSF REU at UTRGV
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.
Previous publications for this program for Summers 2022, 2023 and 2024
PUBLISHED PAPERS
- M Nacianceno, T Oraby, H Rodrigo, Y Sepulveda, J Sifuentes, E Suazo, T Stuck, J Williams, Numerical simulations for fractional differential equations of higher order and a wright-type transformation, Partial Differential Equations in Applied Mathematics, 100751, 2024.
- Miller, E.M., Chan, T.C.D., Montes-Matamoros, C., Sharif, O., Pujo-Menjouet, L., and Lindstrom, M.R., "Oscillations in neuronal activity: a neuron-centered spatiotemporal model of the Unfolded Protein Response in prion diseases" (2024, Bulletin of Mathematical Biology)
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Adjibi, K., Martinez, A., Mascorro, M., Montes, C., Oraby, T. F., Sandoval, R., Suazo, E. (2024). Exact solutions of stochastic Burgers–Korteweg de Vries type equation with variable coefficients. Partial Differential Equations in Applied Mathematics, 100753.
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S. Iftikhar, M. Lembeck, T. Oraby, A. Oseinkwantabisa, A. Sow, And E. Suazo. Using Convolutional Neural Networks to Predict the Order of Fractional Partial Differential Equations. Submitted.
The University of Texas Rio Grande Valley REU Program on Applied Mathematics and Computational and Data Science
