Francesco’s research falls into the broad areas of artificial intelligence and data science, with special emphasis on algorithmic aspects, i.e., on formulating, theoretically characterizing, and designing effective yet efficient algorithms for (novel) problems that are useful to gain insights/information/knowledge from data. As far as data types, special emphasis has been given to graphs, text, and temporal data, but he has also dealt with Euclidean data, probabilistic data, and semistructured data. Large-scale data processing and combinatorial optimization are frequently-occurring keywords in his work.
More specifically, his recent research interests include:
- Graph mining and graph learning
- graph embedding; graph neural networks; explainability in graph learning; graph similarity learning; mining and learning on blockchain graphs; dense-subgraph discovery; mining/querying information-rich graphs (e.g., temporal/knowledge//multi-layer/uncertain graphs); reachability/distance queries on graphs; graph pattern mining; graph clustering; querying graph databases; link prediction
- Natural Language Processing (NLP)
- word embeddings; privacy-preserving text analysis; sentiment analysis; text summarization; document categorization; entity recognition and disambiguation; information extraction from text
- AI in finance
- (Social) Web mining
- social-network analysis; information propagation in online services; community search/detection; social-influence analysis; personalization of online services; log analysis
In the past he has also focused on topics/problems such as mining high-dimensional and multifaceted data (i.e., projective/subspace clustering, clustering ensembles, projective clustering ensembles), uncertainty in data mining & machine learning, time-series data management, bioinformatics, XML data management.