Dr. Rachel Cummings is an Associate Professor of Industrial Engineering and Operations Research at Columbia University. She is also an Affiliate in the Department of Computer Science (by courtesy) and a Co-chair of the Cybersecurity Research Center at the Data Science Institute. Before joining Columbia, she was an Assistant Professor of Industrial and Systems Engineering and (by courtesy) Computer Science at the Georgia Institute of Technology. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and public policy. 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.
Dr. Cummings received her Ph.D. in Computing and Mathematical Sciences from the California Institute of Technology, her M.S. in Computer Science from Northwestern University, and her B.A. in Mathematics and Economics from the University of Southern California. She is the recipient of an NSF CAREER award, a DARPA Young Faculty Award, an Apple Privacy-Preserving Machine Learning Award, a JP Morgan Chase Faculty Award, a Google Research Fellowship, a Mozilla Research Grant, the ACM SIGecom Doctoral Dissertation Honorable Mention, the Amori Doctoral Prize in Computing and Mathematical Sciences, a Simons Award for Graduate Students in Theoretical Computer Science, and Best Paper Awards at SaTML (2023), CCS (2021) and DISC (2014). Dr. Cummings also serves on the ACM U.S. Technology Policy Council, the IEEE Standards Association, and the Future of Privacy Forum’s Advisory Board.
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News
- Nov 2023 – I’m co-organizing NYC Privacy Day on Dec. 4, 2023. Please register and join us!
- Oct 2023 – Our paper “Centering Policy and Practice: Research Gaps around Usable Differential Privacy” was published at IEEE TPS
- Sept 2023 – Our papers What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy and “Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy” were presented at TPDP in a joint oral presentation
- Sept 2023 – Our paper “An active learning framework for multi-group mean estimation” was accepted to NeurIPS
- Sept 2023 – Jayshree Sarathy and Tamalika Mukherjee joined as postdocs, and Shlomi Hod joined as a visiting Ph.D. student!
- June 2023 – Our paper What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy was accepted to USENIX Security
- May 2023 – Congratulations to Roy Rinberg for successfully defending his M.S. thesis! In the fall he will start his Ph.D. at Harvard
- May 2023 – Our paper The Privacy Elasticity of Behavior: Conceptualization and Application was accepted to EC
- Apr 2023 – I was awarded an Early Career Faculty Impact Fellowship for bridging privacy technologies and policymaking
- Feb 2023 – Our paper Optimal Data Acquisition with Privacy-Aware Agents won the Best Paper Award at SaTML
- Jan 2023 – I was selected as a Center for Democracy & Technology Fellow for 2023-2025
- Jan 2023 – Our paper Differentially Private Synthetic Control was accepted to AISTATS
- Jan 2023 – I’ve been promoted to Associate Professor