Rachel Cummings is a Ph.D. Candidate in Computing and Mathematical Sciences at the California Institute of Technology. Her 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. She received her B.A. in Mathematics and Economics from the University of Southern California and her M.S. in Computer Science from Northwestern University. She won the Best Paper Award at the 2014 International Symposium on Distributed Computing, she serves on the ACM U.S. Public Policy Council's Privacy Committee, and she is the recipient of a Simons Award for Graduate Students in Theoretical Computer Science.