Ruobin Gong

Ruobin Gong is an Associate Professor of Statistics at Rutgers University, with a status-only appointment at the University of Toronto's Department of Statistical Sciences. She holds a PhD from Harvard University and focuses on foundational statistical theory and privacy-aware methodologies. Her research integrates Bayesian and imprecise probability frameworks with differential privacy challenges, emphasizing ethical data science practices. Gong's work includes developing the dapper R package for private posterior estimation and organizing events like the Annual Symposium on Applications of Contextual Integrity and NBER workshops on privacy in applied research.

Her research interests span theoretical foundations of uncertainty reasoning (including Bayesian methodology, random sets, and Dempster-Shafer theory) and practical applications in privacy-preserving statistical inference. She serves as an associate editor for Harvard Data Science Review, JASA/TAS Reviews, and Statistics and Public Policy, while also contributing a monthly column, 'Sound the Gong,' to the IMS Bulletin. Gong's recent work addresses challenges in privacy-aware computation, invariant-constrained data sanitization, and the philosophical implications of credence dynamics.

Her contributions include pioneering subspace differential privacy frameworks and formal privacy analyses of historical data swapping methods. Gong actively engages in interdisciplinary collaborations, bridging statistical theory with real-world policy applications through workshops and publications on topics like algorithmic fairness and disclosure avoidance systems.