Fabrizio Silvestri
Fabrizio Silvestri is a Full Professor and the coordinator of the Ph.D. in Data Science, at Dipartimento di Ingegneria informatica, automatica e gestionale (DIAG) of the University of Rome, La Sapienza. His research interests lie in Artificial Intelligence, and in particular, Fabrizio Silvestri deals with machine learning applied to web search problems and natural language processing. He is the author of more than 150 papers in international journals and conference proceedings. It holds nine industrial patents. He is the holder of the "test-of-time" award at the ECIR 2018 conference for an article published in 2007. He is the holder of three best paper awards and other international awards. Fabrizio Silvestri spent eight years abroad in industrial research laboratories (Yahoo! and Facebook). At Facebook AI, Fabrizio Silvestri has directed research groups to develop artificial intelligence techniques to combat malicious actors who use the Facebook platform for malicious purposes (hate speech, misinformation, terrorism, etc.) Fabrizio Silvestri has a Ph.D. in computer science awarded by the University of Pisa with a thesis entitled: "High-Performance Issues in Web Search Engines: Algorithms and Techniques."
- Test of Time Award for the paper “Sorting Out the Document Identifier Assignment Problem”. The paper was selected among those published more than 10 years before and according to the nomination the paper represents a mileston in Inverted Index compression. Nowadays, no paper on inverted index compression is published without firstly applying the technique presented in that paper.
- Nominated for the Super Star Award at Yahoo. Fewer than 10% of Yahoos are nominated for Super Star each year. Super Stars are Yahoos who go above and beyond expectations, contribute to products that make Yahoo the world’s indispensable guide to digital information, and exude positivity that brings out the best in others.
- Yahoo Tech Pulse 2014 Best Paper Award for the paper What App Are You Going to Open Next? Personalized Mobile App Prediction for Aviate.
- Yahoo Patent Milestone Award in recognition of my Yahoo! Patent Inventions.
- Recipient of the Yahoo Labs Excellence Award Program (LEAP) for my work with the Advertising science team that has been included in production code.
- One of the recipients of the 2011 Yahoo! Faculty Research and Engagement Program (FREP) for the Computational Advertisement, Machine Learning and Search & Web Mining Category (http://labs.yahoo.com/node/596)
- Young Investigator Award at ISTI-CNR. Most scientifically prolific young scholar in 2010.
- Best Paper Award Runner Up at 4th ACM Conference on Web Search and Data Mining (WSDM 2011) with the paper “Identifying Task-based Sessions in Search Engine Query Logs”. Co-authors: Claudio Lucchese, Salvatore Orlando, Raffaele Perego, Gabriele Tolomei.
- Best Paper Award at 29th European Conference on Information Retrieval (ECIR 2007) with the paper “Sorting Out the Document Identifier Assignment Problem”.
- Finalist for the ERCIMs Cor Baayen Award: selected as one of the two representatives from Italy for year 2005.
- Best Paper Award at 2004 IEEE/WIC/ACM International Conference on Web Intelli- gence (WI 2004) with the paper “An Online Recommender System for Large Web Sites”. Co-author: Ranieri Baraglia.
Membro di
Ultime pubblicazioni
| A Reproducible Analysis of Sequential Recommender Systems in IEEE ACCESS | 2025 |
| Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News Without Modifying it in Machine Learning and Principles and Practice of Knowledge Discovery in Databases | 2025 |
| Eco-Aware Graph Neural Networks for Sustainable Recommendations in Recommender Systems for Sustainability and Social Good. RecSoGood 2024. | 2025 |
| Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks in IEEE ACCESS | 2025 |
| Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems in ACM TRANSACTIONS ON RECOMMENDER SYSTEMS | 2025 |
| STLight: A Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal Joint Processing in Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 | 2025 |
| A topological description of loss surfaces based on Betti Numbers in NEURAL NETWORKS | 2024 |