Adapting and expanding machine learning solutions to apply to particle physics experiments. Research focuses on neutrinos and studying neutrino oscillation, a unique phenomena that shows neutrinos deviate from their Standard Model prediction, with MircoBooNE and DUNE. To improve oscillation measurements on IceCube, Jessie created and optimized new neural network (regressions and classifications) to reconstruction sparse neutrino events on IceCube and applied this to analyze neutrino oscillation over 9 years of IceCube data. Jessie’s machine learning interests focus on adapting the forefront of ML solutions to noisy, sparse neutrino data; they hope to explore topics of robustness, systematics aware learning, fand advanced neural networks to adapt to neutrino experiments.Dual PhD in Physics and Computational Mathematics, Science, and Engineering from Michigan State University. Bachelor in Science from the University of Michigan, majored in Physics and Screen Arts & Cultures, and minored in Chemistry.Have experience working on more than a dozen film sets, both professional and student productions, mainly in the grip & electric department, camera, and editing. Continue to explore creative opportunities, including 3D printing, makerspaces, and more.