Giorgio Vinciguerra

Giorgio Vinciguerra

Research Fellow

Università di Pisa

Biography

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.

In 2022, I received a Ph.D. in Computer Science from the University of Pisa 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 Ph.D. thesis in Theoretical Computer Science by the Italian Chapter of the European Association for Theoretical Computer Science.

My research falls under the SoBigData.it project funded through the Italian National Recovery and Resilience Plan (PNRR) supported by the European NextGenerationEU programme, the Multicriteria data structures project funded by the Italian Ministry of University and Research, and the SoBigData++ European H2020 project.

Interests
  • Compact data structures
  • Algorithm engineering
  • Data compression
Education

  • Ph.D. in Computer Science, 2018–21

    University of Pisa

  • M.Sc. in Computer Science, 2016–18

    University of Pisa

  • B.Sc. in Computer Science, 2013–16

    University of Pisa

Publications

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

PDF Cite DOI Block-$ε$ tree code LZ$_ε$ code

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.

PDF Cite Code DOI Experiments code & datasets

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.

PDF Cite Code Slides DOI

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.

PDF Cite Code DOI Experiments code & datasets

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

PDF Cite Code Slides Video

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

PDF Cite Code Project Slides Video DOI

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

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Projects

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.

Talks

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)
The design of learning-based compressed data structures
16 Aug 2021 14:30 (UTC+02) — 14:50 (UTC+02)   LADSIOS Workshop, co-located with VLDB 2021
Slides
A tutorial on learning-based compressed data structures
12 Apr 2021 08:00 (UTC+02) — 09:00 (UTC+02)   Seminar at the University of Melbourne
See all talks

Teaching & Supervision

Teaching assistant for:

I co-supervised these 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.

Knowledge is like a sphere; the greater its volume, the larger its contact with the unknown.

― Blaise Pascal

Contact