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Thesis title: End-to-End Imitation Learning for Optimal Asteroid Proximity Operations

Research Area: Machine Learning for End-to-End Controls, with a focus on improving the safety of ML-based controllers. Currently working on Imitation Learning for Optimal End-to-End Asteroid Proximity Operations, where a model is trained to output optimal control commands from raw sensor data for navigation near asteroids.

Expected graduation date: MS in Aerospace Engineering in Fall of 2024 - PhD in Aerospace Engineering in Spring of 2028

2D Histogram for analyzing correlation between optimal and generated control outputs.

Visualization of lidar sensors in Gazebo simulation environment