| KEYWORD |
Analysis of comorbidity networks via persistent homology
Parole chiave BIG DATA ANALYSIS, COMPUTATIONAL TOPOLOGY, ALGORITHMS DEVELOPMENT, MULTIVARIATE STATISTICS, NETWORKS THEORY
Riferimenti FRANCESCO VACCARINO
Gruppi di ricerca Geometria algebrica computazionale e differenziale
Tipo tesi MULTIDISCIPLINARE
Descrizione Topological data analysis (TDA) has been fruitfully applied to enrich the paradigm of network theory. In this thesis the candidate will be requested to use TDA and to implement related SW in python or C++, to analyze data coming form huge comorbidity networks. Please find linked an example of the techniques and setting already developed by our group.
Vedi anche http://rsif.royalsocietypublishing.org/content/11/101/20140873.full
Conoscenze richieste Network or graph theory, statistics, linear algebra. Good programming skills in Python and/or Matlab and/or R. C++ is fine but not compulsory.
Note Previous knowledge of algebraic topology, in particular of homology of simplicial complexes, are a plus.
Scadenza validita proposta 24/02/2016
PROPONI LA TUA CANDIDATURA