Recent & Upcoming Invited Talks

2023

Advances in data-aware compressed-indexing schemes for integer and string keys

Compressed-indexing schemes for a collection of keys are the backbone of modern data systems, and their space and query time efficiency …

2022

Learning-based approaches to compressed data structures design

An emerging trend in data structures design consists in introducing learned components so to uncover some new regularities in the input …

Learning-based approaches to compressed data structures design

An emerging trend in data structures design consists in introducing learned components so to uncover some new regularities in the input …

Extraction of Functional Knowledge from Large Biological Graphs and Applications in Precision Medicine

Huge amounts of molecular, genotypic and phenotypic data are continuously produced and stored in public databases. Although this may …

Learned Indexes

A rigorous approach to design learned data structures

A newly proposed paradigm in data structures design consists in integrating machine-learning components that allow faster queries and …

2021

The design of learning-based compressed data structures

We revisit some fundamental and ubiquitous problems in data structures design, such as predecessor search and rank/select primitives, …

A tutorial on learning-based compressed data structures

Introduction to Wheeler Graphs

Theory and practice of learning-based compressed data structures

We revisit two fundamental and ubiquitous problems in data structure design: predecessor search and rank/select primitives. We show …

2019

Life sciences and algorithmic design: speed and accuracy in small space

The contributions given by Combinatorial Algorithms to the Life Sciences are un-controversially of strategic importance, both in terms of design principles and of habilitating technologies, e.g., BLAST is among the most referenced paper in Science and most used programs in Genomic Analysis. Yet, with the progress of ground-breaking laboratory techniques, unprecedented amounts of data need to be processed and stored, more and more outside of classic research labs. That is, the “1,000 dollars genome and 100,000 dollars analysis” scenario envisioned by E.

DNA combinatorial messages and Epigenomics: The case of chromatin organization and nucleosome occupancy in eukaryotic genomes

Epigenomics is the study of modifications on the genetic material of a cell that do not depend on changes in the DNA sequence, since those latter involve specific proteins around which DNA wraps. The end result is that epigenomic changes have a fundamental role in the proper working of each cell in Eukaryotic organisms. A particularly important part of Epigenomics concentrates on the study of chromatin, that is, a fiber composed of a DNA-protein complex and very characterizing of Eukaryotes.

Hybrid Data Structures and beyond

The ever growing need to efficiently store, retrieve and analyze massive datasets, originated by very different sources, is currently made more complex by the different requirements posed by users, devices and applications. Such a new level of complexity cannot be handled properly by current data structures for Big Data problems. To successfully meet these challenges, new surprising results have appeared recently in the literature that integrate classic approaches (such as B-trees) with various kinds of learning models (such as Neural Networks), called Hybrid Data Structures.

The evolution of searching data structures

In this talk I will survey over 60 years of Research in algorithms and data structures for searching large collections of data. Starting from the classic work about tries (60), we will make a tour that will show how research and applications mutually inspired each other leading to the recent results on compressed data structures, and even more recently, to the surprising results about learned data structures, in which machine learning and classic data structures interplay together.