Teaching Philosophy

I aspire to teach, mentor and guide the next generation of STEM students in fully removing the ‘black box’ of machine learning. Specifically, my goal is to offer students a solid foundation in machine learning when applied to physics, biology, and chemistry. Illuminating the black box requires an interdisciplinary approach to machine learning. To facilitate this I aim to equip students with the tools and skills of machine learning from the viewpoint of its foundations, the biological, chemical and physical sciences. 

To drive my students' curiosity I encourage multidisciplinary exploration, comparing and contrasting various machine learning approaches to data generated by computational methods they may have seen from earlier course work regardless of their majors.

Illustrating to students how methods from a variety of sciences form the foundational theories behind many classic machine learning papers it removes the black box unveiling the deeper connections to students' own diverse backgrounds. Students who have a clear and explainable view of machine learning will be better equipped to identify, critique, and apply machine learning principles, and most importantly identify the limitations. This broad set of tools will help them make advancements, work, and discoveries in tomorrow's workforce and research labs.

*Gemini has been used to review this post for typos and alignment and formatting. Actual prompt discussion is available upon request.