I am a Research Fellow (academic rank in Italy: RTD-A) at the Department of Computer Science of the University of Pisa since January 2023, where I am a member of the A³ Lab.
My research interests include compact data structures, data compression, and algorithm engineering, with a focus on the so-called learned data structures, that is, data structures that exploit machine learning tools to uncover new regularities in the input data and achieve significantly improved space-time trade-offs over traditional ones.
I obtained my PhD from the University of Pisa in February 2022 with a thesis on Learning-based compressed data structures that was awarded the Best PhD thesis in Theoretical Computer Science by the Italian Chapter of the EATCS. Before my current position, I was a postdoc (2022) and PhD student (2018–21) at the University of Pisa. I was a visiting researcher at KTH Royal Institute of Technology (2024) and at Harvard University (2020).
Results of my research, including my software libraries, have found applications in database systems, information retrieval systems, and bioinformatics tools. Furthermore, I was granted an Italian patent (owned by the University of Pisa).
My research is supported by the EU-funded projects SoBigData.it and SoBigData++. In the past, I was supported by the Multicriteria data structures project funded by the Italian Ministry of University and Research.
Program committees:
WSDM 2025, WSDM 2024, AIME 2024, BIBM 2024, WSDM 2023, BIBM 2023
Reproducibility committees:
SIGMOD 2024
Organising committees:
SPIRE 2023
Journal reviewer:
IEEE Trans. Knowl. Data Eng., IEEE Trans. Cloud Comput., IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., Inf. Syst., VLDB J., J. Inf. Secur. Appl., Software: Pract. Exp., Neurocomputing, PLOS ONE
Conference reviewer:
SIGIR 2024, SPIRE 2023, SAND 2023, ALENEX 2022, DCC 2022, LATIN 2022
Teacher:
Teaching assistant:
Co-supervised theses:
A monotone minimal perfect hash function that learns and leverages the data smoothness.
Compressed rank/select dictionary based on Lempel-Ziv and LA-vector compression.
Implementation of two LZ-End parsing algorithms.
Proof-of-concept extension of the PGM-index to support fixed-length strings.
Compressed string dictionary based on rear-coding.
Compressed rank/select dictionary exploiting approximate linearity and repetitiveness.
Compressed bitvector/container supporting efficient random access and rank queries.
Python library of sorted containers with state-of-the-art query performance and compressed memory usage.
Data structure enabling fast searches in arrays of billions of items using orders of magnitude less space than traditional indexes.
C++11 implementation of the Cache Sensitive Search tree.
Python library to build and train feedforward neural networks, with hyperparameters tuning capabilities.
Knowledge is like a sphere; the greater its volume, the larger its contact with the unknown.
― Blaise Pascal