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Combining network-automata and neural networks for data analysis

Grant number: 23/07241-5
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): July 01, 2023
Effective date (End): December 31, 2024
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computational Mathematics
Acordo de Cooperação: Research Foundation - Flanders (FWO)
Principal Investigator:Odemir Martinez Bruno
Grantee:Gilberto Medeiros Nakamura
Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:21/08325-2 - An analysis of network automata as models for biological and natural processes, AP.R


This post-doc project, to be executed into the Scientific Computing Group (SCG), aims to harness the synergy of cellular automata, machine learning, and complex networks to advance data analysis and pattern recognition techniques. The primary objective is to create novel tools for characterizing and extracting meaningful metrics from complex networks, utilizing network-automata and neural networks, to reveal latent data behavior. The project further endeavors to identify the most informative measures through pattern recognition tasks, delineating the optimal descriptors. Two key goals are the investigation and proposal of network-automata models for characterizing dynamic network evolution, and the development of techniques rooted in neural network models for learning features from complex networks and their dynamic evolution. The project's findings have vast application potential, offering substantial contributions to pattern recognition, data science, and understanding of epidemic and social processes, thereby enhancing decision-making in various domains. (AU)

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