Leonardo Pellegrina

Research interests

My research focuses on developing scalable and statistically sound algorithms for extracting meaningful information from large datasets and complex networks. I work on pattern discovery, sampling-based data mining, graph centrality and random graph models, with applications in computational biology—including metagenomics and the analysis of cancer evolution. My goal is to combine strong theoretical guarantees with practical methods that can handle modern, large-scale data.

Prospective students and collaborators

I am always looking for curious and motivated students interested in working with me, at all levels (BSc and MS theses, and Ph.D. positions).

I am also hiring postdocs, supported by my STARS Starting Grant project AtHeNA: Algorithms for Heterogeneous Network Analysis. Topics include (but are not limited to): algorithms for graph mining, fairness in network analysis, realistic random graph and information-diffusion models.
Feel free to contact me with a short description of your background and research interests to know more.

Publications

Sampling Random Graphs from the Colored Configuration Model

Leonardo Pellegrina. Proceedings of the 32nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2026).

DOI arXiv Code

Few-Shot Resampling for Scalable Statistically-Sound Data Mining

Leonardo Pellegrina, Fabio Vandin. Proceedings of the 32nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2026).

DOI arXiv Code

Fast Percolation Centrality Approximation with Importance Sampling

Antonio Cruciani, Leonardo Pellegrina. Proceedings of the IEEE International Conference on Data Mining (ICDM 2025).

DOI arXiv Code

Scalable Rule Lists Learning with Sampling

Leonardo Pellegrina, Fabio Vandin. Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024).

DOI arXiv Code

Efficient Discovery of Significant Patterns with Few-Shot Resampling

Leonardo Pellegrina, Fabio Vandin. Proceedings of the VLDB Endowment 17 (10), (2024).

DOI arXiv Code

SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds

Leonardo Pellegrina, Fabio Vandin. ACM Transactions on Knowledge Discovery and Data Mining (2023).

DOI arXiv Code

Efficient Centrality Maximization with Rademacher Averages

Leonardo Pellegrina. Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2023).

DOI arXiv Code

Discovering Significant Evolutionary Trajectories in Cancer Phylogenies

Leonardo Pellegrina, Fabio Vandin. Bioinformatics (Proceedings of ECCB 2022).

DOI Code

SPRISS: Approximating Frequent k-mers by Sampling Reads, and Applications

Diego Santoro, Leonardo Pellegrina, Matteo Comin, Fabio Vandin. Bioinformatics (2022).

DOI Code

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato. ACM Transactions on Knowledge Discovery and Data Mining (2022).

DOI Code

SPRISS: Approximating Frequent k-mers by Sampling Reads, and Applications

Diego Santoro, Leonardo Pellegrina, Fabio Vandin. Proceedings of the 25th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2021).

arXiv Code

Rigorous and Efficient Algorithms for Significant and Approximate Pattern Mining

Leonardo Pellegrina. Ph.D. thesis, 2021.

PDF

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020).

DOI arXiv Code

Efficient Mining of the Most Significant Patterns with Permutation Testing

Leonardo Pellegrina, Fabio Vandin. Data Mining & Knowledge Discovery 34 (2020)..

DOI Code

Fast Approximation of Frequent k-mers and Applications to Metagenomics

Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin. Journal of Computational Biology Special Issue of Best Papers from the RECOMB 2019 conference, 27, 4, (2020).

DOI Code

SPuManTE: Significant Pattern Mining with Unconditional Testing

Leonardo Pellegrina, Matteo Riondato, Fabio Vandin. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2019).

DOI Code

Multiple Hypothesis Testing and Statistically-sound Pattern Mining

Leonardo Pellegrina, Matteo Riondato, Fabio Vandin. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2019).

DOI

Fast Approximation of Frequent k-mers and Applications to Metagenomics

Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin. Proceedings of the 23th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019).

DOI arXiv Code

Efficient Mining of the Most Significant Patterns with Permutation Testing

Leonardo Pellegrina, Fabio Vandin. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018).

DOI Code

See also my Google Scholar profile.