Giorgio Vinciguerra

Giorgio Vinciguerra

Postdoc

Università di Pisa

Biography

I’m a postdoctoral researcher in Computer Science at the University of Pisa, currently a member of the A³ Lab led by Prof. Paolo Ferragina.

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.

My research falls under a national project named Multicriteria data structures funded by the Italian Ministry of University and Research and under the European H2020 project named SoBigData++.

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

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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Projects

PrefixPGM

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

RearCodedArray

Compressed string dictionary based on rear-coding.

Block-ε tree

Block-ε 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

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
Theory and practice of learning-based compressed data structures
19 Mar 2021 10:00 (UTC+01) — 11:00 (UTC+01)   Seminar at Université de Lille and Inria
Slides
Learning-based compressed data structures (Poster)
25 Feb 2021 13:15 (UTC-08) — 25 Feb 2022 14:15 (UTC-08)   Stanford Compression Workshop 2021
Poster
See all talks

Teaching & Supervision

Teaching assistant for:

I co-supervised:

  • 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