PhD in Computer Science, University of Pisa. I work on continual learning and parameter-efficient fine-tuning (PEFT) of large foundation models: keeping ViTs, LLMs, and multimodal models useful over time without retraining them from scratch.
- Adapter merging for continual learning: HAM, my hierarchical adapter merging method, groups and concatenates LoRA adapters by similarity to scale to long task sequences
- Few-shot domain-incremental learning: adapting foundation models from as few as 1-8 labeled examples
- LLMs over long horizons: in-context interference and context summarization for chat models
- PEFT for everything: LoRA, adapters, prompts, and how to compose them as models and tasks keep changing
- HAM: Hierarchical Adapter Merging for Scalable Continual Learning — arXiv:2509.13211
- PGO-BEN: Proxy-Guided Orthogonalization and Beta Ensembling for Few-Shot Domain-Incremental Learning — TMLR 2026
- Parameter-Efficient Continual Fine-Tuning: A Survey — arXiv:2504.13822
- Adaptive LoRA Merging for Efficient Domain Incremental Learning — NeurIPS 2024 Workshop on Adaptive Foundation Models
- The Future of Continual Learning in the Era of Foundation Models — TCAI Workshop, ICLR 2025 (arXiv:2506.03320)
Full list on Google Scholar.
PyTorch · HuggingFace Transformers · Avalanche · PyTorch Lightning · LangChain · Weights & Biases · SLURM (Cineca Leonardo, A100s) · Django · PostgreSQL
Soccer, Naruto Shippuden, One Piece, Writing Haiku.
models forget fast a small adapter remembers what the weights let go