CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação (FEEC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
received the B.S. (2011), M.Sc. (2013) and Ph.D. (2017) degrees in Electrical Engineering from the University of Campinas (UNICAMP), and performed a sandwich Ph.D. (2016) at GIPSA-lab, France. Currently, he is an Assistant Professor at School of Electrical and Computer Engineering (FEEC) at the same university. His main research interests are machine learning, computational intelligence, information-theoretic learning, unsupervised learning, blind source separation and brain-computer interfaces. (Source: Lattes Curriculum)
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Electroencephalographic (EEG) data are records of brain electrical activity, with potential application in a wide range of contexts, ranging from medical diagnosis, assistive technologies and rehabilitation to entertainment devices. In that sense, with the consolidation of signal processing and machine learning techniques, the direct information transmission system between the brain and t…
Brain-Computer Interfaces (BCI) has been subject of great attention for its potential applications in a myriad of contexts, for example, in assistive, rehabilitation and entertainment technologies. Significant advances, such as data collect with non-invasive methods by means of electroencephalograms (EEG), motivates the study and development of this promising interface. However, the high …
Electroencephalographic (EEG) data are recordings of the brain's electrical activity and have a wide range of applications, from medical diagnostics and assistive technologies to rehabilitation and entertainment devices. In this context, with the advancement of signal processing and machine learning techniques, one system that stands out is the direct communication pathway between the bra…
Multispectral and hyperspectral imaging (MSI/HSI) have advanced consistently in recent decades. As they allow for more robust acquisition of spectral information, their scopes have expanded beyond remote sensing, now finding applications in medicine, food engineering and preservation of cultural heritage.In general, MSI/HSI cameras possess acquisition mechanisms that are prone to being re…
Electroencephalographic (EEG) data are records of brain electrical activity, with potential application in a wide range of contexts, ranging from medical diagnosis, assistive technologies and rehabilitation to entertainment devices. In that sense, with the consolidation of signal processing and machine learning techniques, the direct information transmission system between the brain and t…
(Only some records are available in English at this moment)
Artificial Neural Networks (ANN) have reached noticeable performance in many applications. In the context of supervised learning, network training is done traditionally using the backpropagation algorithm. However, this algorithm has some limitations, among which stand out its high convergence rate to local solutions and the disadvantage of assuming a fixed topology for an ANN. In this co…
Brain-Computer Interfaces (BCI) has been subject of great attention for its potential applications in a myriad of contexts, for example, in assistive, rehabilitation and entertainment technologies. Significant advances, like collecting data with non-invasive methods by means of electroencephalograms (EEG), motivates the study and development of this promising interface. However, the broad…
Errors in digital data are a common issue faced by computational systems when dealing with tasks such as transmission and storage of information. Recently, in certain contexts, such as approximate computing, a considerably high level of errors might be tolerated in order to achieve lower power consumption. In these cases, an error control method is required. An option is the error control…
(Only some records are available in English at this moment)
The Blind Source Separation problem is a model that can be applied to solve several real-world problems that are modeled as a mixture of independent components. Unlike its linear case, a general solution framework for the Nonlinear Blind Source Separation (NLBSS) problem has not been achieved yet. Some approaches based on constrained models have been proposed so far, however, the success…
The nonlinear blind source separation (BSS) problem is a challenging research topic and, differently from the linear case, its development has not reached a general separation framework. Among the reported nonlinear models, we highlight two instances that are crucial for the present project: (i) post-nonlinear (PNL) mixtures and (ii) contexts of separation of colored sources using signal …
| 2 / 2 | Completed research grants |
| 4 / 4 | Ongoing scholarships in Brazil |
| 7 / 5 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 15 / 13 | All research grants and scholarships |
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