Has B. Sc. degree in Computer Engineering (2016), M. Sc and Ph.D. degree in Electrical Engineering (2019), both from University of Campinas (UNICAMP). Research interests: unsupervised signal processing, predictive deconvolution, blind source separation, adaptive filtering and fairness machine learning. (Source: Lattes Curriculum)
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We define a signal, in a broad sense, as an entity that carries information. To better analyze the information of interest, a range of techniques have been developed, especially the ones based on signal decomposition. These techniques are used according to the features we want to extract. Formally, they are implemented by means of matricial or tensorial decomposition. In our MSc research …
One of the most traditional problems in signal processing is the deconvolution, in which the objective is to retrieve a signal that has been distorted by a given system. This problem is still very challenging in its unsupervised version, where the retrieval is carried without knowledge of the system and with access only to some information about the original signal's structure. Among many…
In this work various techniques of blind source separation applied to convolutive mixtures will be studied. The work will be composed by a theoretical study about the topic, by the realization of comparative simulations between the separation techniques, both in the abscence and in the presence of noise, by the analysis of the results and by the development of partial and final reports.
Machine Learning Models have received a lot of attention in a variety of problems due to their great performances and ability to learn from data. But the application of such models in social-economic problems---credit assessment, recidivism prediction, students selection, among others--- has raised ethical concerns. Biased data usually leads the model to biased decisions, which will produ…
| 3 / 3 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
| 4 / 4 | All Scholarships |
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