(she/her/hers)
Harvard University
data privacy, differential privacy, privacy-preserving data analysis
Wanrong Zhang is a Postdoctoral fellow in the theory group at Harvard John A. Paulson School of Engineering and Applied Sciences mentored by Dr. Salil Vadhan. She is a member of the Harvard Privacy Tools Project and the OpenDP project. Her research interests lie primarily in data privacy, with connections to statistics and machine learning. Prior to joining Harvard, she received her Ph.D. from Georgia Tech, where she was advised by Dr. Rachel Cummings and Dr. Yajun Mei. She was selected as a rising star in Data Science by UChicago in 2020. She is a recipient of the Computing Innovation Fellowship from CCC/CRA/NSF.
Bridging Theory and Practice of Privacy-preserving Data Analysis
Given recent concerns centered around large-scale data collection and surveillance, the production of privacy-preserving tools can help assuage public fears involving the misuse of personal data. The field of differential privacy offers data analysis tools that provide powerful worst-case privacy guarantees. Major companies and organizations have deployed differentially private tools. The transition of differential privacy from the academy to practice introduces many new technical challenges. My work addresses the following three challenges: private statistical online-decision making problems, attribute leakage and protection which is beyond differential privacy, and composition theory for interactive differentially private mechanisms.