Physicist | Particle Physics
Find Out More!Bachelor of Science in Applied Physics with Departmental Honors, from UToledo
Nominated Fall 2022 Outstanding Graduating Student in UToledo Department of Physics and Astronomy and School of Natural Sciences and Mathematics
Currently pursuing a Physics PhD at The University of Notre Dame
Graduated with B.S. in Fall 2022, Magna Cum Laude
4 Years experience with LINAC at UToledo
Experience with large data sets from HEP experiments with ANL (CERN/ATLAS) and CERN/CMS.
Computational Atomic Physics with GRASP2018 and SRIM
Particle Physics Simulation with GRASP, SRIM, MadGraph, GEANT4 (etc.)
ML/AI Experience in HEP Analyses
Vacuum/Gas Systems
General Operation of a LINAC
Extensive experience in operating and troubleshooting lab PCs
Extensive experience in programming (C/C++, Python, ROOT/PyROOT)
Data analysis using various tools (ROOT/PyROOT, Origin)
Joined the AMO Research group at UToledo... (rest of text)
These projects involved experimental measurements of emission spectra and lifetimes of neutral Chlorine and Sulfur II. This later included the development of software (PyBeaming2) for simulating the beam-foil interaction present in the THIA experiment, as well as numerical calculations utilizing GRASP2018.
Near the end of my time at UToledo, I worked in collaboration with Dr. Randall Ellingson performing proton irradiation experiments on CdTe solar cells utilizing THIA. This involved adapting the accelerator for ion implantation experiments. I also learned how to characterize materials using SRIM (Stopping and Ranges in Matter).
In summer 2020, I did a virtual internship with ANL. I was assigned a project in collaboration with CERN.
The goal of this project was to test whether Lossy compression has a negative effect on ATLAS data. During this internship I learned how to interface with large servers, basic concepts of High Energy Physics, the PyROOT interface, and data analysis for large data sets.
This is a Beyond the Standard Model (BSM) search for new Higgs-like particles (Xaa), inspired by a 2HDM+S model. This follows a standard CMS search strategy, utlizing a Boosted Decision Tree for selections and using ML for background estimation. Planned to become a Run2 + Run3 analysis.