Data Science Institute

Jennifer Roloff’s research is focused on improving our understanding of the Standard Model of particle physics, and searching for physics beyond this. In particular, her work emphasizes the novel use of jets through the use of new observables, machine learning, and reconstruction techniques. Currently, she is a member of the CMS collaboration, and she was formerly a longtime member of the ATLAS collaboration.

Roloff completed undergraduate studies at the University of Michigan, and obtained her Ph.D. in experimental particle physics at Harvard University. She went on to hold a postdoctoral appointment at Brookhaven National Laboratory, after which she joined Brown University as an assistant professor.

How does your research, teaching, or other work relate to data or computational science?

I work in the area of high energy physics, which involves large, complicated datasets that we need to process in order to perform our research. The techniques that I use are closely tied to data science, including the use of machine learning algorithms, data cleaning, and more.