Rachel Cummings

ASSISTANT PROFESSOR OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH

315 S.W. Mudd

Mail Code: 4704

Research Interests

Data privacy, differential privacy, algorithmic economics, machine learning, statistics

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, JP Morgan Chase Faculty Award, a Google Research Fellowship for the Simons Institute program on Data Privacy, a Mozilla Research Grant, the ACM SIGecom Doctoral Dissertation Honorable Mention, the Amori Doctoral Prize in Computing and Mathematical Sciences, a Caltech Leadership Award, a Simons Award for Graduate Students in Theoretical Computer Science, and the Best Paper Award at the 2014 International Symposium on Distributed Computing. Cummings also serves on the ACM U.S. Public Policy Council's Privacy Committee.

RESEARCH EXPERIENCE

  • Assistant Professor, School of Industrial and Systems Engineering, Georgia Tech, 2017-2020

PROFESSIONAL EXPERIENCE

  • Assistant Professor, School of Industrial and Systems Engineering, Georgia Tech, 2017-2020

PROFESSIONAL AFFILIATIONS

  • Member, Association of Computing Machinery (ACM)
  • Member, Society for Industrial and Applied Mathematics (SIAM)
  • Member, Institute for Operations Research and the Management Sciences (INFORMS)
  • Advisory Board Member, U.S. Census Atlanta Regional Data Center (ARDC)
  • Member, ACM U.S. Public Policy Council
  • Member, IEEE Standards Association
  • Advisory Board Member, Future of Privacy Forum

HONORS & AWARDS

  • NSF CAREER Award, 2020
  • JPMorgan Faculty Award, 2020
  • Google Research Fellowship (through Simons Institute for the Theory of Computing), 2019
  • Mozilla Research Grant, 2018
  • ACM SIGecom Doctoral Dissertation Honorable Mention, 2018
  • Amori Doctoral Prize in Computing and Mathematical Sciences, 2017
  • Caltech Leadership Award, 2017
  • Simons Award for Graduate Students in Theoretical Computer Science, 2015
  • DISC Best Paper Award, 2014