Cummings’ research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and information theory. Her work has focused on problems such as strategic aspects of data generation, incentivizing truthful reporting of data, privacy-preserving algorithm design, impacts of privacy policy, and human decision-making.

Prior to coming to ISyE, Cummings received her B.A. in mathematics and economics from the University of Southern California (2011), her M.S. in computer science from Northwestern University (2013), and her Ph.D. in computing and mathematical sciences from the California Institute of Technology (2017).

Cummings won the Best Paper Award at the 2014 International Symposium on Distributed Computing, and she was the recipient of a Simons Award for Graduate Students in Theoretical Computer Science.

She serves on the ACM U.S. Public Policy Council's Privacy Committee.