I am a data-driven graduate student pursuing an M.S. in Statistics and Data Science at UTSA, with a strong foundation in mathematics, applied statistics, and machine learning.
My work focuses on turning complex, real-world data into actionable insights through statistical modeling, exploratory analysis, and predictive analytics. I have hands-on experience building end-to-end data projects — from data acquisition and cleaning to model development, evaluation, and deployment — using tools such as Python, R, SQL, Power BI, Tableau, and Streamlit.
I have worked with structured and unstructured data across domains including human resources, supply chain, energy, social media, cybersecurity, and music analytics. I enjoy creating reproducible analyses and interactive visualizations that bridge the gap between technical rigor and practical decision-making.
Outside of data work, I build and maintain cloud infrastructure — including a self-hosted private cloud on AWS — and leverage generative AI tools to accelerate development workflows.
Academic Background
Relevant Work
- Collaborating with agile student teams of up to 4 to analyze complex, real-world datasets and engineer applied AI solutions for corporate partners.
- Preparing data-driven frameworks and predictive outputs to present actionable findings at the culminating program symposium.
- Created Python and Excel dashboards to automate grading workflows and visualize performance insights, improving efficiency by 30%.
- Held 8+ weekly office hours and provided tutoring to 30+ students weekly, covering R, Python, SAS, Tableau, and PostgreSQL.
- Developed and enhanced 20+ course materials using Microsoft Office and R Markdown to support diverse learning styles.
- Spearheaded 20+ finite element simulations to optimize aerospace bracket designs, achieving a 69% reduction in mass while maintaining structural integrity.
- Applied data-driven optimization techniques using Python and R to analyze stress data, validate mesh convergence, and back engineering decisions with reproducible metrics.
- Analyzed simulation outputs and stress data using Excel and validated mesh convergence using adaptive refinement methods (h-adaptive and p-adaptive), enhancing precision and computational efficiency.
- Processed data from 300+ sessions via Excel (VLOOKUP, pivot tables) to track student progress with 99% accuracy, providing front-line support to 50+ students weekly.
- Tutored over 100 undergraduate students in mathematics, statistics, and programming, driving an estimated 15–20% average improvement in academic performance.
- Provided front-line academic support and service to 50+ students per week via phone and in person, addressing course challenges, scheduling conflicts, and university procedures.