Leonardo Pellegrina

Leonardo Pellegrina is an Assistant Professor (RTT) at the Department of Information Engineering (DEI) of the University of Padua, where he joined in March 2023 and advanced to a tenure-track position in March 2025. He is a member of the AIDA Lab and previously completed his Ph.D. at the same institution under Prof. Fabio Vandin, with a visiting research period at Brown University's Department of Computer Science under Prof. Eli Upfal from December 2018 to July 2019.

His research focuses on developing practical and theoretically sound algorithms for Data Mining and Computational Biology, with particular expertise in pattern mining, graph algorithms, and cancer phylogenetics. His work bridges theoretical computer science with real-world applications in biology and healthcare, emphasizing statistical rigor and computational efficiency.

Analysis of his recent publications reveals a strong trend toward statistically-sound pattern mining with applications in computational biology. His research combines advanced sampling techniques, Rademacher averages, and hypothesis testing to develop efficient algorithms for large-scale data analysis, with significant contributions to cancer evolution modeling and metagenomics.

His scientific achievements include:

• Teaching Award of Merit from University of Padova (November 2024)
• Honorable mention for 2021 SIGKDD Dissertation Award
• Best PC member of ACM The Web Conference 2023
• Best PC member of ACM The Web Conference 2022
• RECOMB 2019 Travel Fellowship

Dr. Pellegrina has secured significant research funding including a Senior type B grant (December 2022) and a Junior type B grant (December 2020) from the Department of Information Engineering. His collaborative work includes supervision of students like Sijing Tu and extensive collaboration with researchers including Fabio Vandin, Matteo Riondato, and Diego Santoro.

As part of the AIDA Lab at the University of Padua, he contributes to a vibrant research environment focused on data science and artificial intelligence applications, with particular emphasis on biomedical data analysis and algorithm development.