Manuel Huth

This paper presents a framework for integrating single-cell and bulk data using nonlinear mixed-effect models and tailored parameter estimation, improving parameter identifiability and prediction accuracy.

The paper develops a federated Difference-in-Differences method for privacy-preserving causal impact evaluation using the DataSHIELD platform, demonstrating its effectiveness on simulated and real-world data.

This study introduces an attack that reconstructs privacy-protected data in FL frameworks by exploiting covariance information and proposes defense strategies using differential privacy.