Determination of genes potentially responsive to ionizing radiation through machin...
Causal inference and machine learning on complex climatic systems
Development and Implementation of Data Augmentation Methods for Predictive and Gen...
Grant number: | 19/23293-0 |
Support Opportunities: | Regular Research Grants |
Duration: | September 01, 2020 - August 31, 2022 |
Field of knowledge: | Physical Sciences and Mathematics - Physics - General Physics |
Principal Investigator: | Francisco Aparecido Rodrigues |
Grantee: | Francisco Aparecido Rodrigues |
Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Abstract
Complex systems are made of discrete parts that interact in a non-linear way. The Internet, social networks and ecosystems are examples of complex systems. A big challenge in this area is the prediction of the dynamical processes, such as epidemic spreading and synchronization of coupled oscillators, from the network structure. In the present project, we aim to perform the prediction and inference of dynamical processes in networks. We will consider methods of Bayesian inference to determine which network properties most influence dynamical processes simulated on the network structure. Machine learning approaches will be adopted to perform the prediction of dynamical processes, including epidemic spreading and synchronization. Applications, such as medicine, epidemiology and link prediction, will also be studied in this project. The goals proposed here will open a new field that considers machine learning and statistics to model and predict complex systems. (AU)
Articles published in Agência FAPESP Newsletter about the research grant: |
More itemsLess items |
TITULO |
Articles published in other media outlets ( ): |
More itemsLess items |
VEICULO: TITULO (DATA) |
VEICULO: TITULO (DATA) |