Giorgio Vinciguerra

Giorgio Vinciguerra

Research Fellow (RTD-A)

Università di Pisa

I’m a Research Fellow (RTD-A) at the Department of Computer Science of the University of Pisa, where I am a member of the A³ Lab.

I received a PhD in Computer Science from the University of Pisa in 2022, under the supervision of Prof. Paolo Ferragina, with a thesis on Learning-based compressed data structures, that is, data structures that achieve new space-time trade-offs compared to traditional solutions by learning, in a rigorous and efficient algorithmic way, the regularities in the input data with tools from machine learning and computational geometry. The thesis was awarded the Best PhD thesis in Theoretical Computer Science by the Italian Chapter of the EATCS. During my PhD, I spent a research period at Harvard University, hosted by Prof. Stratos Idreos.

My research is supported by: the SoBigData.it project funded by the European Union - NextGenerationEU - Italy’s National Recovery and Resilience Plan (PNRR) and the SoBigData++ project funded by the European Union - Horizon 2020. In the past, I was supported by the Multicriteria data structures project funded by the Italian Ministry of University and Research.

Interests
  • Data structures
  • Data compression
  • Algorithm engineering
Education

  • PhD in Computer Science, 2018–21

    University of Pisa

  • MSc in Computer Science, 2016–18

    University of Pisa

  • BSc in Computer Science, 2013–16

    University of Pisa

Publications

Marco Costa, Paolo Ferragina, Giorgio Vinciguerra (2024). Grafite: taming adversarial queries with optimal range filters. PACMMOD.

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Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra (2024). CoCo-trie: data-aware compression and indexing of strings. Inf. Syst..

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Paolo Ferragina, Mariagiovanna Rotundo, Giorgio Vinciguerra (2023). Engineering a textbook approach to index massive string dictionaries. SPIRE.

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Paolo Ferragina, Marco Frasca, Giosuè Cataldo Marinò, Giorgio Vinciguerra (2023). On nonlinear learned string indexing. IEEE Access.

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Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, Giorgio Vinciguerra (2023). Learned monotone minimal perfect hashing. ESA.

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Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra (2022). Compressing and querying integer dictionaries under linearities and repetitions. IEEE Access.

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Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra (2022). Compressed string dictionaries via data-aware subtrie compaction. SPIRE.

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Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra (2022). A learned approach to design compressed rank/select data structures. ACM Trans. Algorithms.

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Giorgio Vinciguerra (2022). Learning-based compressed data structures. Ph.D. thesis.

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Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra (2021). Repetition- and linearity-aware rank/select dictionaries. ISAAC.

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Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra (2021). On the performance of learned data structures. Theor. Comput. Sci..

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Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra (2021). A “learned” approach to quicken and compress rank/select dictionaries. ALENEX.

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Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra (2020). Why are learned indexes so effective?. ICML.

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Paolo Ferragina, Giorgio Vinciguerra (2020). The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. PVLDB.

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Paolo Ferragina, Giorgio Vinciguerra (2020). Learned data structures. Recent Trends in Learning From Data (Springer).

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Talks

Learned monotone minimal perfect hashing
15 Aug 2023 14:00 (UTC+02) — 15:00 (UTC+02)   BARC Talk
Advances in data-aware compressed-indexing schemes for integer and string keys
7 Mar 2023 14:40 (UTC+01) — 15:15 (UTC+01)   A Tutorial Workshop on ML for Systems and Systems for ML, co-located with BTW 2023
Slides
Learning-based approaches to compressed data structures design
22 Aug 2022 14:30 (UTC+02) — 15:00 (UTC+02)   IGAFIT Workshop for Algorithms Postdocs in Europe (AlgPiE by IGAFIT 2022)
Learning-based approaches to compressed data structures design
20 Jul 2022 17:00 (UTC+02) — 17:30 (UTC+02)   The future of compressed data structures, 20 years after the FM-index
A rigorous approach to design learned data structures
10 May 2022 16:30 (UTC+02) — 17:00 (UTC+02)   3rd European Meeting on Algorithmic Challenges of Big Data (ACBD 2022)
See all talks

Service

Program committees:
WSDM 2024, AIME 2024, BIBM 2024, BIBM 2023, WSDM 2023

Organising committees:
SPIRE 2023

Journal reviewer:
IEEE Trans. Knowl. Data Eng., IEEE Trans. Cloud Comput., IEEE Trans. Comput.-Aided Des. Integr. Circuits 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

Teaching & Supervision

Teacher:

Teaching assistant:

Co-supervised theses:

  • Marco Costa, Engineering data structures for the approximate range emptiness problem, MSc in Computer Science - ICT, 2022.
  • Mariagiovanna Rotundo, Compressed string dictionaries via Rear coding and succinct Patricia Tries, MSc in Computer Science - ICT, 2022.
  • Antonio Boffa, Spreading the learned approach to succinct data structures, MSc in Computer Science - ICT, 2020.
  • Alessio Russo, Learned index per i DB del futuro, BSc in Computer Science, 2020.
  • Lorenzo De Santis, On non-linear approaches for piecewise geometric model, MSc in Computer Science - AI, 2019.

Software

LeMonHash

LeMonHash

A monotone minimal perfect hash function that learns and leverages the data smoothness.

LZ$\phantom{}_{\boldsymbol\varepsilon}$

Compressed rank/select dictionary based on Lempel-Ziv and LA-vector compression.

LZ-End

Implementation of two LZ-End parsing algorithms.

PrefixPGM

Proof-of-concept extension of the PGM-index to support fixed-length strings.

RearCodedArray

Compressed string dictionary based on rear-coding.

Block-$\boldsymbol\varepsilon$ tree

Block-$\boldsymbol\varepsilon$ tree

Compressed rank/select dictionary exploiting approximate linearity and repetitiveness.

LA-vector

LA-vector

Compressed bitvector/container supporting efficient random access and rank queries.

PyGM

PyGM

Python library of sorted containers with state-of-the-art query performance and compressed memory usage.

PGM-index

PGM-index

Data structure enabling fast searches in arrays of billions of items using orders of magnitude less space than traditional indexes.

CSS-tree

CSS-tree

C++11 implementation of the Cache Sensitive Search tree.

NN Weaver

NN Weaver

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

Contact