Angela Violi

The Violi Lab carries out cross-disciplinary research at the intersection of nanoscience and data science. By integrating machine learning techniques with molecular simulations, the team strives to unravel fundamental scientific principles while tackling practical problems in material science, healthcare, and environmental sustainability. Their methodological toolkit encompasses various cutting-edge approaches: active learning and Bayesian experimental design to improve sample efficiency; advanced gradient boosting techniques for predictive modeling; specialized neural networks to decode protein-nanoparticle interactions; and Lasso-like algorithms for feature selection and regularization. Through this integrated approach, the lab aims to make significant contributions to both scientific understanding and technological innovation.