Fall 2024 – Present / San Antonio, TX
Vascular Biomechanics and Biofluids Laboratory – UTSA – Graduate Research Assistant I
· Investigate the relationship of node specific Geometric Indices in Abdominal Aortic Aneurysm (AAA) and Rupture Potential Index(RPI), using Graph Neural Networks (GNNs).

Spring 2022 – Fall 2024 / San Antonio, TX 
Advanced Data Engineering Laboratory – UTSA – Graduate Research Assistant I
· Developed a novel Graph Neural Network (GNN) model augmented with graph Laplacian regularization to analyze complex relationships between multiple chronic conditions (MCC) and individual risk factors. Focused on holistic analysis of prevalent conditions such as diabetes, obesity, cognitive impairment, hyperlipidemia, and hypertension within the Cameron County Hispanic Cohort.
· Enhanced MCC prediction by creating a two-stage graph generative model, incorporating a Graph Variational Auto-Encoder (GVAE) and a Laplacian Regularized GNN. Aimed at optimizing patient data representation and improving early identification of at-risk individuals, emphasizing refined graph structure for precise pattern recognition in medical data.
· Investigate the influence of object shape on leeway drift using a deep learning model trained on a unique dataset incorporating physical and environmental variables. Demonstrated superior predictive performance in estimating leeway speed and drift components, outperforming traditional machine learning models.