I am a physics and computational mathematics, science, and engineering PhD candidate at Michigan State University. Why the two departments? It is because I am a dual PhD student which means I complete some of the requirements of each department for my classwork and comprehensive exam and my thesis fulfills the requirements of both departments. It's like double majoring at the PhD level.
My current research uses machine learning to analyze applications to physics graduate programs. I hope to determine the factors most predictive of an applicant being accepted into the program. I am advised by Dr. Danny Caballero.
I earned my bachelor's degree in physics and astonomy/astrophysics at (The) Ohio State University. I also minored in mathematics and cognitive science. While at Ohio State, I worked with Dr. Andrew Heckler on projects related to oscillatory motion. I was also a tutor in the Mathematics and Statistics Learning Center, focusing mainly on calculus.
Outside of my research, I am interested in science communication and teaching. Doing science communication work was never part of "my plan." However, after learning about Astrobites and wishing there was (and then creating) a physics education research version (PERbites), I have become very interested in science communication. Outside of PERbites, I serve as the vice president of marketing for MSU's science communication organization (MSU SciComm) and am a pen pal for Letters to a Pre-Scientist.
As a graduate student, I have been a teaching assistant for Lyman Briggs 273 and 274 (physics 1 and physics in Lyman Briggs College at MSU). These are studio physics courses for mainly life science majors In a studio class, lecture, lab, and recitation are combined into a single session. This means that class would begin with a short lecture, then students would investigate some concept using lab equipment, and then complete more typical recitation style problems based on their findings in a single period. As these courses were for life science majors, many of the problems were focused on life science concepts. For example, diffusion can be modeled as a conservation of momentum and collisions problem.