Giorgio Vinciguerra

Giorgio Vinciguerra

PhD candidate

Università di Pisa

Biography

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

I’m working 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
  • M.Sc. in Computer Science, 2018

    University of Pisa

  • B.Sc. in Computer Science, 2016

    University of Pisa

Papers

Repetition- and linearity-aware rank/select dictionaries. ISAAC, 2021.
On the performance of learned data structures. Theor. Comput. Sci., 2021.
Learning Based Compressed Data Structures. Stanford Compression Workshop, 2021.
Why are learned indexes so effective?. ICML, 2020.
Learned data structures. Recent Trends in Learning From Data (Springer), 2020.

Projects

Block-ε tree

Block-ε tree

A compressed rank/select dictionary exploiting approximate linearity and repetitiveness.

LA-vector

LA-vector

A 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

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

CSS-tree

CSS-tree

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

NN Weaver

NN Weaver

A Python library to build and train feedforward neural networks, with hyperparameters tuning capabilities.

Talks

The design of learning-based compressed data structures
A tutorial on learning-based compressed data structures
Theory and practice of learning-based compressed data structures
Learned indexes
The PGM-index: a multicriteria, compressed and learned approach to data indexing

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