Proximal algorithms offer state of the art performance for many large scale optimization problems. In recent years, the proximal algorithms landscape has simplified, making the subject quite accessible to undergraduate students. Students are empowered to achieve impressive results in areas such as image and signal processing, medical imaging, and machine learning using just a page or two of Python code. In this talk I'll discuss my experiences teaching proximal algorithms to students in the Physics and Biology in Medicine program at UCLA. I'll also share some of my teaching philosophy and approaches to teaching undergraduate math courses. Finally, I'll discuss my own research in optimization algorithms for radiation treatment planning, which is a fruitful source of undergraduate research projects.