-Developed and trained classification models in sklearn, PyTorch, and TensorFlow which labeled synthetic radionuclide datasets with 90 percent accuracy.-Created complex data visualizations using pandas, NumPy, and Matplotlib to compare efficacy of ML models.-Implemented continuous integration and other features to the project’s Git repository, speeding up development.-Streamlined key processes in sample generation, increasing transferability of synthetic data to real-world application.-Authored two scientific papers in applied Machine Learning.