Jordan Awan

Jordan Awan works at the intersection of statistics and data privacy, developing mathematical methods for making valid statistical inferences (such as hypothesis tests, confidence intervals, and Bayesian estimation) from data that has been intentionally perturbed to protect individuals' privacy, a technique known as differential privacy. This research aims to help analysts draw trustworthy conclusions from privatized datasets without needing access to the original, unprotected data. The work is largely theoretical and methodological, contributing statistical tools and guarantees for the growing field of privacy-preserving data analysis.

Publication output has grown from a low base around 2017-2018 to a sustained higher level of roughly 7-10 papers per year since 2022, suggesting a steady and active publication pace over the past decade.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026