Hi, I'm Jordan!

I grew up in Washington state and received a Bachelor's of Science in Physics and Mathematics from Seattle University in 2016. At SU, I contributed to research projects in quantum chromodynamics and computational neuroscience, and I also served as a teaching assistant for undergraduate physics, astronomy, and mathematics courses.

In 2016, I entered the joint PhD program in Computational Science in the Computational Science Research Center at San Diego State University and Claremont Graduate University in California. During my PhD, I was supported by the USDOE Office of Science Graduate Fellowship, working in tandem with the theoretical nuclear physics group at Lawrence Livermore National Laboratory. My dissertation focused on data-driven methods for theoretical nuclear physics: deep learning models for nuclear scattering data libraries, and uncertainty quantification for HPC-scale calculations in the nuclear shell model.

After graduation, I joined the theoretical physics group at Argonne National Laboratory as a postdoctoral research associate. There, I implemented neural network-based improvements for HPC-scale quantum Monte Carlo simulations.

While at Argonne, I decided I wanted to pivot my research focus from physics into computational neuroscience. In 2024, I joined the lab of Jose Peña in the Department of Neuroscience at Albert Einstein College of Medicine in New York City as a postdoctoral research associate. Jose's lab investigates the neural mechanisms of sound localization in the barn owl, and I have developed a robust computational pipeline for high-resolution biophysical simulation of auditory neurons in the barn owl's sound-localization system. This work was supported by the BRAIN Initiative.

In my future work, I am interested in developing new methods and software tools for neuroscience research, including multi-scale modeling and statistical analysis of neural circuits.