Home

I have a newly minted PhD in astrophysics and I’m looking for a role in data science/machine learning. I’ve spent the past several years developing Monte Carlo population synthesis simulation codes, building data analysis pipelines and automated data visualization suites, performing statistical analysis and modeling, and extracting meaningful insights from model and real-world data. I’m excited to bring my experience to new challenges outside of academia!

Take a look at my projects and resume to see what I’ve been working on in data science, machine learning, and data visualization. Like what you see? Contact me!

Astrophysics

My PhD dissertation is Massive Stars Behaving Badly: Exceptional Interactions And Their Remnants and is about what happens massive stars (those with masses 8x or more greater than our Sun!) get themselves into sticky situations. I was advised by Richard O’Shaughnessy in the Center for Computational Relativity and Gravitation at Rochester Institute of Technology through the Astrophysical Sciences and Technology program. I study massive stars using tools like McFACTS (and here, I’m also one of the lead developers!), COMPAS, and MESA.

My MSc thesis is Spindown and Envelope Inflation of Massive Main Sequence Stars in the Milky Way and studies spindown and envelope inflation of massive stars. I completed my MSc in Astrophysics at the Argelander Institute for Astronomy at the University of Bonn with Professor Norbert Langer. I completed my BS in Physics at Virginia Tech in the Physics Department.