06/03/2026
From healthcare to finance, organizations across industries depend on sensitive data to make important decisions.
Recent PhD graduate Cecilia Ferrando researches differential privacy, a mathematical framework that enables decision-makers to learn from data while protecting individual privacy. She defended her dissertation, “Differentially Private Statistical Learning: Uncertainty Estimation and Utility Preservation,” which develops practical methods for privacy-preserving machine learning.
Ferrando will join LinkedIn as a Senior Machine Learning Engineer on its Core AI team. Congratulations, Cecilia!