Prior Scientific Work

Disk-Galaxy Stellar Bar Evolution

My early PhD research has focused on exploring different elements of galactic bar evolution through use of large N-body simulations and through observations of gas rich, high redshift (z~2) barred galaxies with my PhD advisor, Prof. Elena D'Onghia. In the theoretical work I have collaborated with scientists and faculty at the Center for Computational Astrophysics through their 2022 Big Apple Galactic Dynamics Summer School, Prof. Martin Weinberg, Dr. Jason Hunt, and Prof. Kate Daniel. During this time, I developed a package to efficently access and analyze the particle trajectories of the 1 billion particle simulation detailed in Hunt et al. 2021, SMUDGE, hosted on SciServer with the code detailed on GitHub. In the observational work, I led observations of the 30m IRAM radio dish telescope and the reduction of accompanying data from the NOEMA interferometric array in collaboration with Prof. Mauro Giavalisco and Dr. Zhiyuan Ji. 

Below images show my visit to the 30m IRAM radio dish telescope outside of Granada, Spain.

Spatially Resolved Galaxy Observations with MaNGA

Dark Gaps and Bar Resonances 

Worked with Dr. Dhanesh Krishnarao to compare resonances within the GALAKOS high resolution N-body simulation of a Milky Way-like galaxy to the the disk galaxies in the MaNGA SDSS sample. This work revealed that photometric "dark-gaps" correspond to the 4:1 ultraharmonic resonance rather than the location of the galactic-bar corotation. This has implications for deducing bar parameters and inferring mass distributions in disk galaxy observations. This work is published in April 2022 in the Astrophysical Journal  (929, 112).

Measuring Bar Pattern Speeds

Extended a method developed by Dr. Thomas Peterken to measure the pattern speed of the spiral arms and test density wave theory using MaNGA galaxy observations for use on barred galaxies. This work was completed with the guidance of Prof. Matthew Bershady.

EEG Analysis and Stimulation of Slow Wave Sleep Stage for Alzheimer's Disease Treatment

Developed and optimized algorithms to perform real-time brain activity tracking and triggering of stimulation in sleeping patients through EEG monitoring to stimulate Slow Wave Sleep waveforms to facilitate on-going memory formation. I was connected with this job based on my demonstrated expertise in signal processing and hardware-software interfacing in an instrumentation course. In this role, I wrote Matlab code to filter, monitor, and process the patient brainwaves and trigger the real-time stimulation. I was responsible for the interface of hardware and EEG processing software. I also created a pipeline for data analysis with a GUI interface for the team to use in their further analysis of patient data throughout the study. The results of this study were published (after I had began my astronomy PhD) in the peer reviewed journal SLEEP.

The image below shows an example of the sleep wave-form and the detection of SWS waveform to trigger the stimulation and the poster shows a scientific result regarding the kinds of waveforms that are indications of SWS.