![Shutterstock Data Turbulence Image](/sites/default/files/styles/965_wide/public/2022-03/Shutterstock%20Data%20Turbulence.jpg?itok=EXMHe0IH)
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.