As ML methods continue to grow in popularity and adoption, researchers are starting to identify new and interesting ways to learn from and interact with their (ever-growing) scientific data. As a result, it is becoming increasingly important for researchers to be able to work effectively with data in order to gain insight and draw meaningful conclusions about their work.

In this talk I will discuss how my research experience in HEP has prepared me to deal with the computational challenges facing scientific workflows on next-generation HPC systems, explain how I see this relationship as being mutually beneficial, and offer some ideas for future research directions.