I'm a machine learning engineer at Carlson Software and graduated summa cum laude from the University of Kentucky in May 2024. While in school, I was a member of the Lewis Honors College and double-majored in Computer Science and Mathematics. My work at Carlson focuses on self-supervised learning in computer vision tasks like image segmentation and boundary detection. These techniques are applied to aerial images for use by civil engineers in our Carlson IntelliCAD application. In addition to designing and training deep learning models, I've created multiple applications that enable my team to generate and label ground-truth data as well as a custom HTTP server, API, and webpages for automating and visualizing training, inference, and evaluation.

I've previously worked as a research assistant in medical imaging and deep learning at the University of Kentucky, finding novel training methods for image segmentation models. In this capacity, I wrote a paper that was published to ISBI 2024 and presented a poster at UK's 2023 Commonwealth Computational Summit. I was also a research assistant in the field of nuclear physics where I built a high-speed data acquisition system that was deployed in an experiment on the J-PARC particle accelerator. This experiment (NOPTREX) was performed to collect information about gamma radiation emissions from decaying neutrons.

Over the summers of 2022 and 2023, I interned at Lockheed Martin and Infineon Technologies. Through these internships, I gained experience building data analysis and report automation tools as well as developing physical verification rules for transistor-level devices. I also spent four semesters as a teaching assistant and grader at the University of Kentucky where I graded assignments and instructed labs on topics ranging from UI development to intermediate C++ and Linux.