The Links Lecture Award was created in 2017 by the American Statistical Association to honor the significant contributions of three pioneers who advanced the US federal statistical system’s ability to meet data demands—Constance Citro, Robert Groves, and Fritz Scheuren. Each year, the award recognizes an individual, regardless of seniority, who has made a significant contribution to advancing official statistics and whose work and vision offer the promise to advance the role of official statistics in addressing critical data needs.

Praivacy (noun)

Pronounced: /ˈprā-və-sē/

Balancing data confidentiality and utility

October 24, 2025

12:00 - 1:30 pm ET

A Virtual Event

2025 Links Lecturer

Aleksandra (Seša) Slavkovic is a Professor of Statistics & Public Health Sciences, the Dorothy Foehr Huck and J. Lloyd Huck Chair in Data Privacy and Confidentiality, and Associate Dean for Research in the Eberly College of Science at Penn State. Her research focuses on developing methodologies for data privacy and confidentiality, particularly in small- and large-scale surveys, health, genomic, and network data. She has made significant contributions to differential privacy and broad data access, ensuring accurate statistical inference to support reliable science and policy. Slavkovic has held multiple editorial and leadership roles and serves on several advisory committees, including the board of the Society for Privacy and Confidentiality Research, which supports the Journal of Privacy and Confidentiality and related initiatives. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Statistical Institute. She earned her Ph.D. (2004) and M.S. (2001) in Statistics, as well as a Master of Human-Computer Interaction (1999) from Carnegie Mellon University. She holds a B.A. in Psychology from Duquesne University (1996).

Discussant

Saki Kinney is a Senior Research Statistician at RTI International with nearly 20 years of experience in applied research, with emphasis on statistical methods in privacy and confidentiality. Dr. Kinney's background in disclosure avoidance, multiple imputation, and Bayesian methods is complemented by diverse experience in statistical analysis, surveys, and developing and implementing practical solutions for federal and state agencies. She earned her PhD in statistical science from Duke University, MS in statistics from CSU East Bay, and BA in mathematics from UC Berkeley.