Paper 'Balancing Accuracy and Novelty with Sub-Item Popularity' accepted for publication at ACM RecSys 2025; 'DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems' accepted for publication at SIGIR 2025; 'Enhancing Utility in Differentially Private Recommendation Data Release via Exponential Mechanism' accepted for publication at ECIR 2025; 'Are We Done with MMLU?' accepted for publication at NAACL 2025; Presented DataRec project at ACM SIGIR 2025; Tutorial proposal 'Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho (D&D4Rec)' accepted at RecSys 2025.
Research Experience
Researcher at Politecnico di Bari, Italy; Student Volunteer Chair at ACM UMAP 2025; Organizer of DaQuaMRec, the first international workshop on data quality-aware multimodal recommendation, held in conjunction with RecSys 2025.
Education
Ph.D. from Università La Sapienza di Roma, thesis titled 'Designing Secure and Knowledge-Aware Recommender Systems Leveraging Data Properties and Graph Structures'.
Background
Post-doctoral researcher in Artificial Intelligence. Research focuses on Recommender Systems, particularly knowledge-aware recommenders, graph-based recommenders, and the impact of data characteristics on the privacy and robustness of recommenders.