I aspire to teach, mentor and guide the next generation of students in the application of explainable machine learning in the interdisciplinary nature of tomorrow’s STEM fields. Specifically, my goal is to facilitate the integration of computer/data science with physics, biology, and chemistry, fostering a more interconnected and innovative approach to these disciplines. I aim to equip students with the tools and skills to illuminate the black box of machine learning. I hope to drive my students curiosity by allowing them to explore, compare and contrast various machine learning approaches to data generated by computational methods they may have seen from a junior year course. I also hope to illustrate to students how methods from the physical sciences form the foundational theories behind many classic machine learning papers, and I hope to make these connections clear and accessible to my students such that they are more prepared and equipped with a board set of tools to help them make advancements, work, and discoveries in tomorrows workforce and research labs.