| KEYWORD |
Analysis of comorbidity networks via persistent homology
keywords BIG DATA ANALYSIS, COMPUTATIONAL TOPOLOGY, ALGORITHMS DEVELOPMENT, MULTIVARIATE STATISTICS, NETWORKS THEORY
Reference persons FRANCESCO VACCARINO
Research Groups Geometria algebrica computazionale e differenziale
Thesis type MULTIDISCIPLINARE
Description 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.
See also http://rsif.royalsocietypublishing.org/content/11/101/20140873.full
Required skills Network or graph theory, statistics, linear algebra. Good programming skills in Python and/or Matlab and/or R. C++ is fine but not compulsory.
Notes Previous knowledge of algebraic topology, in particular of homology of simplicial complexes, are a plus.
Deadline 24/02/2016
PROPONI LA TUA CANDIDATURA