About Me
Before starting my PhD at Brown, I earned a B.S. in Geological Sciences with a minor in Applied Statistics at Brigham Young University. My early work focused on a more traditional view of Earth science, including geophysical field studies in Hawaii and geochemical analyses of the Rocky Mountains, and so forth. This experience grounded me in Earth science fundamentals while exposing me to the limitations of traditional modeling approaches — limitations that would later motivate my shift toward data-driven methods.
I’ve since worked across academia, government, and industry, including internships and contrcontracted work with NASA and Meta — where I’ve built and deployed ML systems for applications ranging from autonomous air traffic control to geospatial network planning. These experiences sharpened my ability to translate fundamental research into real-world impact.
What’s next?
As I approach the final stages of my Ph.D., I’m looking ahead to the next chapter—one where I can continue building meaningful, technically rigorous tools at the intersection of AI and the natural world.
I’m currently exploring opportunities as an ML Engineer or Data Science Researcher, particularly in climate tech, environmental AI, or adjacent fields where data science can inform sustainability, resilience, and decision-making. I’m especially drawn to teams working on applied scientific problems with a strong engineering backbone—whether that’s in industry, a government lab, or a mission-driven research institute.
That said, I’m open to a wide range of paths. A well-aligned postdoctoral fellowship or a role at a national lab could offer the chance to deepen my work in uncertainty quantification, scalable modeling, or scientific ML. Likewise, joining an industry team focused on AI for environmental, energy, or geospatial applications would allow me to bring research into production and support impactful solutions at scale.