CV Lattes
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Universidade Presbiteriana Mackenzie (UPM). Escola de Engenharia (EE) (Institutional affiliation from the last research proposal) Birthplace: Brazil
graduation at Engenharia de Produção from Universidade Estadual de Campinas (2014), master's at Engenharia de Produção e de Manufatura from Universidade Estadual de Campinas (2017) and doctorate at Electric Engineering from Universidade Estadual de Campinas (2020). Has experience in Production Engineering, focusing on Production Engineering, acting on the following subjects: análise de componentes independentes, gráficos de controle multivariáveis, reconhecimento de padrões, blind source separation and pattern recognition. (Source: Lattes Curriculum)
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Frequently, practical situations require the application of complex machine learning methods to model the relation between input data and the system's output. Generally, these methods are difficult to interpret, which brings a disadvantage when there is a need to understand the impact of attributes on the obtained result. As a solution, recent works have been developing model-agnostic met…
Machine learning techniques have been used in the construction of automatic systems in order to deal with several practical problems. Examples include the applications in credit systems, which evaluate if an individual will lead to a possible default with respect to the received credit, or in judicial systems, which predict whether the defendant under trial may re-offend. In general, the …
Decision making problems involving multiple criteria can be found in several practical situations. Many of these problems have specific characteristics that can lead to biased results, especially with respect to the size of the problem data. For example, when there is a large number of criteria, many of them may not be necessary in terms of expressiveness in the model, or even be correlat…
In signal and image processing there is a wide range of problems that can be formulated as an inverse problem or as a signal separation problem. Very often, these problems are solved by optimizing a criterion which, in the case of an inverse problem, can be a measure of distance between the observed data and the outputs of a considered model, and, in the case of signal separation, a separ…
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Currently, the research community has been putting an effort into the development of mechanisms that improve interpretability and fairness in machine learning problems. Recent works addressed interpretability by means of model-agnostic method called SHAP. This method is based on the Shapley value, a classical concept in cooperative game theory that indicates the marginal contribution of a…
Several practical situations can be modeled as multi-criteria decision making (MCDM) problems. Generally, methods used to deal with MCDM problems do not take into account the data structural information, such as the interaction between criteria. For example, correlated criteria may bias the decision analysis towards a specific alternative. In this context, an approach based on Choquet int…
Control charts are commonly employed to determine whether the variability observed in a process stems from natural causes or assignable causes, such as machine failure. When an assignable cause is detect, it is quite helpful to classify the nature of such a cause, since this information is useful to tackle the original problem. This task can be accomplished by automatic pattern recognitio…
| 1 / 1 | Ongoing grants |
| 1 / 1 | Completed research grants |
| 4 / 4 | Completed scholarships in Brazil |
| 3 / 3 | Completed scholarships abroad |
| 9 / 9 | All research grants and scholarships |
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