Rachel Cummings

Rachel 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.

Specifically, her work focuses on differential privacy, which is a parameterized notion of database privacy that gives a mathematically rigorous worst-case bound on the maximum amount of information that can be learned about an individual's data from the output of a computation. In one thread of her work, Cummings designs differentially private algorithms that enable accurate and privacy-preserving data analysis in a wide variety of computational settings, including machine learning, optimization, statistics, and economics. A second thread of her work uses her interdisciplinary background to address the technical, legal, and social challenges of bringing differentially private tools to bear in practice and at scale.

Cummings received her PhD in Computing and Mathematical Sciences from the California Institute of Technology, her MS in Computer Science from Northwestern University, and her BA in Mathematics and Economics from the University of Southern California. She is the recipient of an NSF CAREER award, a DARPA Young Faculty Award, a DARPA Director's Fellowship, an Early Career Impact Award,  an Apple Privacy-Preserving Machine Learning Award, a JP Morgan Chase Faculty Award, a Google Research Fellowship for the Simons Institute program on Data Privacy, a Mozilla Research Grant, a Provost’s Teaching Award, the ACM SIGecom Doctoral Dissertation Honorable Mention, the Amori Doctoral Prize in Computing and Mathematical Sciences, and Best Paper Awards at DISC 2014, CCS 2021, and SaTML 2023. Cummings also serves on the ACM U.S. Technology Policy Committee, the IEEE Standards Association, and the Future of Privacy Forum's Advisory Board.