Fred Azizi
Statistics · privacy · applied modeling
I work on statistical methods that make data useful while protecting the people behind it.
I'm Fred Azizi, a statistician in the Department of Mathematics and Statistics at UMBC. My research connects differential privacy, Bayesian modeling, and the question of how closely synthetic data can preserve the distribution of the original data. I also teach statistics and work on practical modeling problems.
What I'm working on
Private synthetic data
Studying how moment matching and Wasserstein distance can measure the quality of data released with privacy guarantees.
Bayesian privacy
Exploring how record-level risk can inform privacy-weighted likelihoods and the usefulness of the resulting analyses.
Applied statistics
Building and evaluating statistical models, with an emphasis on clear interpretation and reproducible analysis.
Research and collaboration
I'm interested in conversations about privacy-preserving statistics, Bayesian methods, and applied statistical work. You can reach me at fazizi1@umbc.edu.