Tugg Ernster

Results-oriented individual with a proven track record at the University of Arizona, where I enhanced student understanding in physics through collaborative discussions. Skilled in Python and SQL, I excel in problem-solving and leading teams to surpass goals. Proficient in applying machine learning to complex systems through deep learning architectures.

• Conducting research on leveraging graph neural networks (GNNs) to distinguish between different classes in high-energy particle physics. The goal is to investigate the differences in particle production mechanisms, focusing on identifying key features that differentiate particle interactions in complex physics datasets. This work involves data preprocessing, model development, and evaluating GNN performance on particle collision data to improve classification accuracy and enhance our understanding of particle behavior in high-energy environments.