developed infrastructure code for machine learning model evaluation pipeline, including parsing of models, training data, metrics, automatic plots, and slideshow generation.

project automated a suite of novel, in-depth pipelines; pipelines analyzed machine learning models to evaluate performance and make recommendations for varying use-case scenarios created, documented, and enforced vision for analysis toolkit used by project of 50+ internal employees

contributed to proposals for state-of-the-art artificial solutions for government request-for-proposals (RFPs)

Created lab demonstration to show fault-tolerant computing capability for lunar lander module; demo utilized

Developed proof-of-concept object-detection module to demonstrate small form factor flight computer can run object detection models in real-time; used Tensorflow, NVIDIA Jetson, ROS Melodic, and OpenCV; written in Python/C++

Developed ground station software for tracking autonomous small-UAS vehicles to analyze mid-mission performance

Performed trade study to analyze possible software-in-the-loop testbeds for advanced autonomy UAS program

Developed ROS drivers to integrate various sensors to ROS-based autonomy suite