Undergraduate Research Assistant
Current- Trained a Python Diffrax-based Neural ODE to accurately represent complex orbital motion around various types of regular and irregular celestial bodies, achieving a model loss under 1% in LEO- Constructing a series of databases within Docker containers to visualize complex telemetry streams in Grafana - Simulating satellite flight telemetry, with particular focus on orbital, sensor, and control system data, via Basilisk, with the goal of passing this data through a pipeline to visualization to streamline development and testing of anomaly detection algorithms-Researching Deep Learning algorithms for satellite fault detection to expand the early-detection and autonomy capabilities afforded by classical Machine Learning methodology