Sara Scaramuccia

Sara Scaramuccia
University of Rome Tor Vergata | UNIROMA2 · Dipartimento di Matematica

PhD in Computer Science
Computational Topology, Computational Geometry, Topological Data Analysis, Visual Information, Machine Learning

About

13
Publications
10,086
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95
Citations
Additional affiliations
September 2020 - January 2022
Polytechnic University of Turin
Position
  • Post-doc researcher
September 2016 - January 2017
University of Genoa
Position
  • Research Assistant
Description
  • "Elementi di Matematica e Logica" course for Bachelor student in Computer Science
January 2016 - June 2016
University of Genoa
Position
  • Research Assistant
Description
  • "Geometria" course for Bachelor student in Computer Science
Education
November 2014 - May 2018
University of Genoa
Field of study
  • Computer Science
September 2011 - December 2013
University of Genoa
Field of study
  • Pure Mathematics
September 2007 - November 2011
University of Genoa
Field of study
  • Mathematics

Publications

Publications (13)
Chapter
The aim of this chapter is to give a handy but thorough introduction to persistent homology and its applications. The chapter’s path is made by the following steps. First, we deal with the constructions from data to simplicial complexes according to the kind of data: filtrations of data, point clouds, networks, and topological spaces. For each cons...
Article
Full-text available
We introduce an algorithm to decompose any finite-type persistence module with coefficients in a field into what we call an {\em interval basis}. This construction yields both the standard persistence pairs of Topological Data Analysis (TDA), as well as a special set of generators inducing the interval decomposition of the Structure theorem. The co...
Preprint
Full-text available
We introduce an algorithm to decompose any finite-type persistence module with coefficients in a field into what we call an {\em interval basis}. This construction yields both the standard persistence pairs of Topological Data Analysis (TDA), as well as a special set of generators inducing the interval decomposition of the Structure theorem. The co...
Article
Full-text available
The combination of persistent homology and discrete Morse theory has proven very effective in visualizing and analyzing big and heterogeneous data. Indeed, topology provides computable and coarse summaries of data independently from specific coordinate systems and does so robustly to noise. Moreover, the geometric content of a discrete gradient vec...
Preprint
Full-text available
Understanding choices performed by online customers is a growing need in the travel industry. In many practical situations, the only available information is the flight search query performed by the customer with no additional profile knowledge. In general, customer flight bookings are driven by prices, duration, number of connections, and so on. H...
Article
Persistent homology allows for tracking topological features, like loops, holes and their higher-dimensional analogues, along a single-parameter family of nested shapes. Computing descriptors for complex data characterized by multiple parameters is becoming a major challenging task in several applications, including physics, chemistry, medicine, an...
Preprint
Full-text available
The main objective of this paper is to introduce and study a notion of perfectness for discrete gradient vector fields with respect to (multi-parameter) persistent homology. As a natural generalization of usual perfectness in Morse theory for homology, persistence-perfectness entails having the least number of critical cells relevant for persistent...
Preprint
Full-text available
Persistent Homology (PH) allows tracking homology features like loops, holes and their higher-dimensional analogs, along with a single-parameter family of nested spaces. Currently, computing descriptors for complex data characterized by multiple functions is becoming an important task in several applications, including physics, chemistry, medicine,...
Conference Paper
Full-text available
Multivariate data are becoming more and more popular in several applications, including physics, chemistry, medicine, geography, etc. A multivariate dataset is represented by a cell complex and a vector-valued function defined on the complex vertices. The major challenge arising when dealing with multivariate data is to obtain concise and effective...
Conference Paper
Full-text available
Persistent homology is a powerful notion rooted in topological data analysis which allows for retrieving the essential topological features of an object. The attention on persistent homology is constantly growing in a large number of application domains, such as biology and chemistry, astrophysics, automatic classification of images, sensor and soc...
Conference Paper
Full-text available
We present a new algorithm for computing a discrete gradient field on multivariate data. For multivariate data, we consider a shape with a vector-valued function f defined on it. The proposed algorithm is well suited for parallel and distribute implementations. The discrete gradient field V we obtain is a reduced representation of the original shap...
Poster
Full-text available
A new persistence module-preserving algorithm for a single Morse-based reduction for cubical and simplicial complexes

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