Anna Vettoruzzo is a postdoctoral researcher contributing to the OpenEuroLLM and ELLIOT project, where she focuses on advancing the adaptability, resource efficiency, and generalization capabilities of foundation models. Core methodologies include developing parameter-efficient fine-tuning (PEFT) strategies, continual learning frameworks, and designing novel benchmarks to evaluate agentic and interactive intelligence. She received her Ph.D. in computer science from Halmstad University, Sweden, as part of the Center for Applied and Intelligent Systems Research (CAISR). Her doctoral research focused on enhancing the generalization capabilities of machine learning models through meta-learning, exploring how models can learn to learn with limited data. Her broader research interests include world models, continual learning, self-supervised learning, and in-context learning.