About

Born and raised in Dallas, TX, I fell in love with physics and math at an early age. By the time I started high school, my dad introduced me to Brian Green's "The Elegant Universe" and I was hooked. I graduated from Woodrow Wilson High School salutatorian and headed off to Southwestern University to study physics and mathematics. There I got my first taste of research in building a passive solar-thermal battery and solar concentrator designed to mechanically collect and store energy without the use of any chemical batteries. While this project never quite worked as designed, I learned the important lesson that research you are passionate about is never finished. Also at Southwestern, I did my first exercise in data science by designing a program to predict optimal wind farm output based on historical weather data. That exercise would prove important in the long term.

From Southwestern, I decided to pursue a Master's and Ph.D. in physics at Southern Methodist University. There I found a home in writing software to improve our understanding of the proton by using parton distribution functions (PDFs). These functions represent the probability of certain events happening when two protons collide at nearly the speed of light. Since they can't be measured directly, these PDFs rely heavily on both theoretical predictions and physical data. This means that one needs to understand the underlying physics, data analysis and programming in order to figure them out. For my dissertation, I would unite all three of those into a world first PDF analysis including nuclear data from the Large Hadron Collider.

After my doctorate, I joined the Office of Information Technology at SMU as their Data Science Research Applications Developer, the founding member of their Research Support Team. This team is designed to provide support for research projects and initiatives across the university, focusing mainly on fostering growth in data science, internet of things and high performance computing applications. My position specifically is aimed at leveraging skills from my background in the hard sciences in an interdisciplinary way to support and encourage data-driven research. This approach leads to some incredible opportunities to push boundaries and tackle problems that have never been encountered before and to build solutions that only are possible because of the volume of data being collected in our modern world.