Francisco A. Rodrigues is a Professor at the Institute of Mathematical and Computer Sciences (University of São Paulo), working with complex systems. He is Editor of the Journal Chaos, Solitons and Fractals (Elsevier), Associate Editor of the Journal of Physics: Complexity (AIP Publishing) and Editor of the Journal of Computational Science (Elsevier). He has published more than 120 papers in international scientific journals that have been cited more than 8,000 times (h-index 36). He was chairman of the ICMC research committee for two terms. He is a supervisor (MSc and PhD levels) in the Computer Science and Computational Mathematics Program at ICMC, Physics Graduate Program at IFSC and Statistics Graduate Program (ICMC-UFScar). He was a visiting professor at the University of Warwick (UK) between 2018 and 2019 and a visiting researcher (short term) at the Universidad de Zaragoza (Spain), the Potsdam Institute for Climate Research (Germany), and the Complexity Science Hub Vienna (Austria). He was also a visiting professor at Aschaffenburg University of Applied Sciences (Germany) and Universidad de Zaragoza. His teaching areas include Complex Networks, Statistics, Stochastic Processes, Machine Learning and Data Science. Francisco is also the author of the book "An Introduction to Multiplex Networks: Basic Formalism and Structural Properties", published by Springer in 2018. More information about 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…
The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical imp…
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…
The study of complex systems by moving agents and multilayer networks is very recent and there are several issues to be addressed, as we propose in this project. Particularly, we aim at developing a new model of synchronization of traveling agents, considering different types of interaction and rough landscapes for particles moving. We will be able to determine conditions for phase transi…
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With the growing availability of data from real systems in areas such as biology, sociology, physics, medicine and economy, a proportional expansion on the studies developed in complex networks allowed the rise of a number of techniques for analysis and prediction of real dynamic systems, as done in the fields of information propagation and epidemics in social networks. Alongside those de…
The current research project aims to construct mathematical and computational models in polarized social and interaction networks, using graph theory, complex network theory, and appropriate polarization measures. The main objective is to identify the relationships between network polarization and other phenomena that affect society, such as crises, violent conflicts, and the proliferatio…
Epidemic spread, rumor, and synchronization models are fundamental to the further understanding of dynamic processes. Most models developed on complex networks consider only static structures, where connections are maintained over time. In recent years, models of epidemic propagation in temporal networks have been proposed. In these networks, the connections are not static, but vary duri…
(Only some records are available in English at this moment)
Even though complex networks and dynamical systems are receiving growing attention from the scientific community, there are still few well understood descriptive methods for the realization of graph sampling procedures. The network sampling problem is essential for the study of real world systems, given that, more often than not, the real network of interest is not completely known or the…
A big part of current scientific development is done by collaborations between scientists, which forms a complex network. The main goal of this project is, through link prediction methods and machine learning algorithms, to predict and understand the formation of links between researchers.
Epidemic spreading is ubiquitous in our society and it is fundamental to understand this process in different scenarios. Many mathematical models and simulations have verified that the structure of social networks plays a fundamental role in the spreading of the infectious agent. Thus, if we change the connections between individuals, we can control the propagation. Moreover, human behavi…
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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…
Collective phenomena in the real world often occur in systems that have multiple types of individuals, each one with its behavior and decision-making. Such a feature can be captured with the use of information theory and causal inference tools, which have been intensely used to quantify individuals' influence. We aim at enhancing the understanding of causal reasoning in decision-making pr…
Spreading phenomena in real world often occur in systems that have multiple types of connections, each one with its own structure. Such feature can be captured with the use of multiplex networks, which have been intensely used to support epidemic and rumor models. We propose to enhance the understanding of spreading processes in multiplex environments by two research proposals, using sing…
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| 2 / 2 | Ongoing research grants |
| 10 / 9 | Completed research grants |
| 5 / 5 | Ongoing scholarships in Brazil |
| 32 / 29 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 10 / 10 | Completed scholarships abroad |
| 60 / 56 | All research grants and scholarships |
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CV Lattes
ORCID