I am a Data Engineering student at Texas A&M University, curious about complex systems and driven to make difficult problems clear, useful, and actionable.
My path into engineering has already taken me farther than I expected. During an onsite internship at CERN in Geneva, Switzerland, I contributed to a software development effort connected to MadGraph5 while learning from researchers and engineers from around the world. Visiting the birthplace of the World Wide Web and working in a place where discovery is part of the culture was a dream come true.
Since then, I have continued seeking opportunities that stretch how I think. I have worked on software, data, and machine learning problems across high energy physics, aerospace inspection, healthcare analytics, and computer vision. Each experience has strengthened my ability to enter an unfamiliar domain, learn the language of the problem, and translate complexity into something practical.
At LoneStar NDE, I helped engineers develop standardized inspection reports from robotic scans of carbon fiber rocket structures, organizing scan data, images, plots, and testing details to support the identification and documentation of potential material flaws. At Ascension, I partnered with a clinical data engineer and pediatric clinicians to extract, validate, and visualize Cerner data for agitation, bronchiolitis, and osteomyelitis, creating outcome reporting connecting agitation severity with interventions, measuring adherence to evidence-based care pathways, and identifying opportunities to reduce unnecessary testing and medication use while improving patient outcomes. Through the Aggie Data Science Club, I proposed and led a six-member team exploring sparse image reconstruction with computer vision and machine learning.
I expect to graduate from Texas A&M in May 2028. I am excited to keep building, learning, and contributing to teams that approach difficult problems with curiosity, rigor, and a willingness to improve.