Christopher Woolford

DoD SMART Graduate Fellow, University of Wisconsin-Madison

2024 - Present

Developing a differentiable particle in cell code using JAX.

Python JAX Particle-in-cell Data Analysis High Performance Computing (HPC)

Directed Energy Research Intern, Air Force Research Laboratory

2023-2024

Used a particle-in-cell code called ICEPIC to explore the phase repeatability and sensitivity of high powered microwave (HPM) devices. Conducted analysis of phase noise in both experimental and simulation data.

Python Particle-in-cell Data Analysis High Performance Computing (HPC)

NASA Intern, NASA Glenn Research Center

2022

Developed a deep learning architecture to predict the initial stiffness of a composite material using graph neural networks (GNN). Developed a surrogate model for the micro-scale component for modeling stress-strain curves using a combination of graph neural networks (GNN) and recurrent layers (LSTM) that was able to speed up simulations for composite materials by a factor of 2.

Python Machine Learning Deep Learning High Performance Computing (HPC)

SULI Intern, Fermilab

2022

Contributed to the development of a GEANT4 simulation of the EMPHATIC spectrometer; a particle spectrometer that measures hadron scattering and production cross-sections for future neutrino experiments and BSM searches. Trained graph neural networks (GNN) to reconstruct particle tracks using GEANT4 simulation data.

GEANT4 C++ Data Analysis

Research Assistant - Gulley Optics Lab, Kennesaw State University

2021-2024

Identified factors contributing to the photon drag effect in quantum nanowires induced by femtosecond lasers using numerical simulations of laser propagation and developed a new code to simulate 3D laser propagation using the pseudospectral time domain method. The 3D laser simulation code can effectively model laser propagation in three dimensions.

Fortran Numerical Modeling High Performance Computing (HPC)

Research Assistant - Joffe High Energy Lab, Kennesaw State University

2021-2024

Implemented a Monte Carlo simulation of a radon gas detector using GEANT4 to evaluate its usability for detecting ambient radon gas and analyzed the results using ROOT. The simulation showed that the system was capable of detecting alpha radiation from atmospheric radon, and provided guidance on hardware design.

GEANT4 ROOT C++ High Performance Computing (HPC)

Research Assistant - Computational Healthcare and Biotechnology Lab (COHB), Kennesaw State University

2020-2024

Modeled the gene regulatory networks controlling the expression of genes associated with disease by creating deep learning models using gated recurrent units (GRU) to analyze gene expression data and identify gene regulatory networks using feature attribution. The developed deep learning model is being used to model genes associated with obesity, non-small cell lung cancer, and Parkinson’s disease.

Python Deep Learning Bioinformatics PyTorch PyTorch Lightning HyperOptuna

Wonders of Physics Outreach Fellow, University of Wisconsin-Madison

2024 - 2025

Participated in K-12 outreach activities for public engagment.

Kennesaw State University Peach State NASA Scholar, Kennesaw State University

2021 - 2024

Participated in STEM engagement activities at K-12 institutions.