Developed an innovative algorithm that analyzes over 40,000 satellite images of the Sun from NASA's Solar Dynamics Observatory, achieving an impressive > 90% accuracy in classifying solar flares (B, C, or M class) based on the previous days satellite imagery. This project was demoed at Georgia Tech's inaugural Scientific Machine Learning Symposium. Scientific Machine Learning is a subset of ML that focuses on combining known physics principles with ML to achieve higher accuracy in Scientific models. For this project, this involved adapting a traditional CNN architecture to incorporate essential physics principles (like Wien's Law) that influence the occurrence of a solar flare.