CV Lattes
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Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. Instituto de Ciência e Tecnologia (ICT) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Marcos G. Quiles is an Associate Professor at the Department of Science and Technology, Federal University of São Paulo, Brazil. He received his BS degree, with honors, in 2003 from the State University of Londrina, Brazil, and his M.Sc. and Ph.D. degrees in Computer Science from the University of São Paulo, Brazil, in 2004 and 2009, respectively. From January to July of 2008, Quiles was a Visiting Scholar in the Perception and Neurodynamics Lab at The Ohio State University, Columbus-OH-US. From January to December of 2017, Quiles was an Academic Visitor at the University of York, York-UK. He was awarded a Brazilian research productivity fellowship from the Brazilian National Research Council (CNPq). His research interests include nature-inspired computing, machine learning, complex networks, and their applications in interdisciplinary problems. (Source: Lattes Curriculum)
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Much of the energy consumed in the world is derived directly or indirectly from the burning of carbon-based fossil fuels, which is also the ran material for an extensive chain of products. The burning of carbon-based fossil fuels is directly linked with the increasing concentration of CO2 in the atmosphere, which is one the sources that contribute to the global warning along with CH4. The…
The discovery of new materials is directly linked to the evolution of society. These materials can allow the generation of new drugs to the development of electronic components for clean energy generation. It is noteworthy that in addition to the various materials already available in nature, a multitude of compounds can be theoretically generated from the combination of ordinary chemical…
The main purpose of this project is the development of new techniques for semi-supervised learning-based networks for dynamic data sets. Properties of complex networks that represent the data and dynamic computational models for label propagation will be taken into account. Measures of complex networks will be extracted and used as parameters for selection of vertices. This selection is t…
Explainable Artificial Intelligence (XAI) has gained prominence for providing transparency and understanding in the decision-making processes of machine learning models. This explainability is crucial in scientific areas, where trust in predictions must be high and decisions based on these models can have significant implications. In the field of chemistry, the use of XAI is particularly …
The use of machine learning (ML) in the study and discovery of new materials has emerged as a fundamental approach, offering valuable alternatives and complements to traditional experimental and computational methodologies. ML's ability to analyze vast amounts of data and identify complex patterns enables the efficient prediction of properties and the generation of new materials, signific…
Understanding the properties of a material is essential to evaluate its application. The study of properties can be carried out in various ways, from theoretical investigation to experimental evaluation of the material under analysis. However, the time and cost required to evaluate a particular material can be high, restricting the discovery of new materials to a small portion of the chem…
Materials discovery seeks to generate tailored materials with specific physicochemical properties. This study relies on the field of Material Science, which uses computer simulation methods and laboratory experiments to describe the behavior of the material to different environments and other materials. Nevertheless, computer simulation methods used to investigate such material properties…
The analysis and generation of new materials is a costly process, both from an experimental and computational point of view. Computational methods, such as molecular dynamics and DFT (Density Functional Theory) calculation, have been used to study chemical compounds. However, even using these computational approaches, the screening of materials is still a costly process, considering that …
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Machine learning (ML) methods to reduce time and cost for material discovery have exhibited remarkable achievements. Nevertheless, notably, significant advances have been made for small molecules. Therefore, fields like polymer informatics, which involves designing macromolecules, are still in the early stages of exploring such techniques to learn structure-property relationships. Among t…
The study of real systems via complex networks has been a very active area in recent years. However, much of the progress made during this period is related to the study of the properties of networks with fixed topology (static). Commonly, the study and synthesis of complex networks make use of measures to characterize its main topological attributes. The characterization of the network i…
| 1 / 1 | Ongoing grants |
| 3 / 3 | Completed research grants |
| 2 / 2 | Ongoing scholarships in Brazil |
| 9 / 5 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 17 / 13 | All research grants and scholarships |
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