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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Francisco A. Rodrigues is a full professor at the Institute of Mathematics and Computer Science at the University of São Paulo (ICMC-USP), specialising in complex systems and data science. He is currently Head of the Department of Applied Mathematics and Statistics. He is an editor of Chaos, Solitons Fractals (Elsevier), the Journal of Computational Science (Elsevier), the New Journal of Physics (IOP Publishing) and the Journal of Complex Networks. He is also a co-editor of Europhysics Letters. He is also an associate editor of the Journal of Physics: Complexity (IOP Publishing). He has published over 150 papers in international scientific journals, which have received more than 10,000 citations (h-index 41). He served two terms as Chair of the Research Committee at ICMC, and supervises Master's and PhD students on the Graduate Programmes in Computer Science and Computational Mathematics (ICMC), Physics (IFSC) and Statistics (ICMC-UFSCar). He was a visiting professor at the University of Warwick (United Kingdom) from 2018 to 2019, funded by the Leverhulme Trust. He has also been a short-term visiting researcher at the University of Zaragoza (Spain), the Potsdam Institute for Climate Impact Research (Germany) and the Complexity Science Hub Vienna (Austria). He has also held visiting professor positions at Aschaffenburg University of Applied Sciences (Germany) and the University of Zaragoza. His teaching areas include complex networks, statistics, stochastic processes, machine learning, causality, and data science. He is the author of the book An Introduction to Multiplex Networks: Basic Formalism and Structural Properties, published by Springer in 2018. In 2023, he was included in Elsevier's list of the world's top 2 most influential scientists. Further details of his research can be found at: https://sites.icmc.usp.br/francisco (Source: Lattes Curriculum)
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The Applied Research Center for in Artificial Intelligence, IARA, is formed by a multidisciplinary team, with national experts of internationally recognized for their competence in artificial intelligence, internet of things telecommunications and smart cities. The center, hosted in São Carlos, in the state of São Paulo, will work in network with researchers from all Brazilian regions, or…
Complex networks serve as a representation of the intricate structures found in complex systems, ranging from biological interactions to social connections. The study of these networks has gained significant importance in recent decades, enabling the modelling of organizational structures and dynamic processes in various systems. Over the past years, there has been a growing interest in a…
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 …
In this Project we pretend to introduce and characterize null models, based on random matrix ensembles, for complex networks of current interest. In particular we plan to study: (i) random networks with gain and/or loss, (ii) non-uniform random regular graphs, (iii) bipartite networks, (iv) mutualistic networks, and (v) directed networks. We will perform a scaling analysis to define the u…
(Only some records are available in English at this moment)
Complex networks are graphs composed of vertices interconnected by edges, representing relationships between pairs of objects. These structures are used to describe interactions in complex systems such as social networks, in which vertices are represented by individuals and edges represent some type of social relationship, such as friendship. The spectrum of a network is composed of the e…
Graph embeddings have emerged as powerful tools for representing complex networks in low-dimensional vector spaces while preserving essential topological properties. This project focuses on evaluating and comparing various graph embedding methods in their ability to maintain community structures-subsets of nodes with higher internal connectivity than external. While popular methods such a…
As an effect of climate change, we are increasingly witnessing the occurrence of extreme weather events such as hydrological anomalies, fierce cyclones and heat waves. The socioeconomic impact of such phenomena is immense and there is great scientific interest in understanding their behavior, characterization and forecast, exceptionally challenging tasks considering their extreme behavior…
(Only some records are available in English at this moment)
Parkinson's Disease (PD) is a progressive nervous system brain disorder which affects motor neuron loss control and movement coordination. According to the World Health Organization (WHO), Parkinson's disease affects 1 percent of the world's population over the age of 65, making it the second most common neurodegenerative disease. Its signs and symptoms may vary for each person, making th…
The study of opinion dynamics is extremely relevant for understanding the emergence of the polarization phenomenon in social networks. This project aims to use machine learning to predict with high accuracy dynamic variables associated with the Sznajd model in complex networks. We will consider modeling through complex networks, Monte Carlo simulations, and regression methods. The project…
In this research project, we will investigate the presence of anomalies in the dynamical evolution of general complex systems through recurrence analysis. With the aim of identifying behaviours that differ from the common, or regions of complex networks that possess distinct topological properties, we propose a numerical method based on real data or simulations of nonlinear dynamic system…
(Only some records are available in English at this moment)
In this project, we intend to explore the application of recurrence-based methods for climate data, specifically focusing on detecting relevant conditions and parameters associated with tipping points and extreme events mainly driven by climate change. By integrating insights from nonlinear dynamics with machine learning techniques, we aim to uncover hidden patterns and dynamical anomalie…
Mathematical modeling is essential for understanding spreading processes in complex networks. While much research has focused on static networks, the importance of temporal networks is becoming increasingly recognized. In this project, we aim to develop a novel approach to modeling temporal networks using mobile agents and a movement structure that allows for the creation of dynamic clust…
The interest in learning how rumors spreads in society has led researches to develop theoretical computational models to investigate the underlying mechanisms behind the spread of fake news in our society. Currently, literature on the topic provides a variety of epidemiological and cultural models. While each type of modeling adopts a different approach, all of them aim to resemble some a…
Incidence of infectious diseases as well as the costs and benefits of public health interventions are not evenly distributed across society. In this project, we aim to analyze the geographic and socioeconomic differences in infectious disease incidence and antibiotic prescribing, and measure their respective contributions to the overall variation in antibiotic prescriptions. We also inten…
(Only some records are available in English at this moment)
| 1 / 1 | Ongoing grants |
| 11 / 10 | Completed research grants |
| 5 / 5 | Ongoing scholarships in Brazil |
| 39 / 36 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 12 / 12 | Completed scholarships abroad |
| 70 / 66 | All research grants and scholarships |
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