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Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. Instituto de Biociências, Letras e Ciências Exatas (IBILCE) (Institutional affiliation from the last research proposal) Birthplace: Brazil
bachelor's at Bacharelado em Matemática Aplicada e Computacional from Instituto de Biociências, Letras e Ciências Exatas - UNESP (2007) and master's at Applied Mathematics from Universidade Estadual Paulista Júlio de Mesquita Filho (2010). Has experience in Mathematics, focusing on Digital Image Processing, acting on the following subjects: processamento digital de imagens, eliminação de ruído, modelagem matemática, equações diferenciais parciais and retoque digital. (Source: Lattes Curriculum)
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This project focuses on developing new methodologies and tools based on deep learning in two branches of the Computer Vision field: (i) interactive image segmentation and (ii) detection of deforestation occurrences in the Brazilian Amazon. This multifaceted project aims to advance the forefront of knowledge in the aforementioned branches through ongoing investigations and research efforts…
The Data Science Center for Smart Industry acts as an advanced data analytics hub for industry, aiming to improve operational efficiency, product quality, innovation and sustainability through the application of data science and analytics techniques of data in industrial processes. Contact with industries will be mediated by SENAI and operationalized through workshops and thematic meeting…
This project comprises two distinct research branches: Digital Image Segmentation and Data-Driven Epidemiological Modeling against COVID-19. Our proposal aims at combining theoretical as well as technical advancements as a solution for different applications in the field of Computational Intelligence, whose previous results have been published in high-quality refereed publications such as…
The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies and power suppliers in minimizing the cost of electricity to the end consumer. In this paper, the problem of forecasting the energy demand in the Brazilian Interconnected P…
This scientific initiation project aims to investigate the relationship between wildfires and air quality in the state of São Paulo, by using Exploratory Data Analysis and Machine Learning techniques. The main goal is to identify the variables that most impact the variation in the Air Quality Index (IQAr), as well as to perform predictive analysis of pollutant levels and meteorological va…
Computational Learning-inspired techniques has contributed effectively and transdiciplinarily in Computational Vision and Pattern Recognition applications, such as deforstation mapping in the Amazon biome. In this context, data-driven algorithms have promoted the development of innovative and accurate tools for the detection of deforested areas, by analyzing time series of remotely sensed…
In the speculative and competitive environment such as the energy market, predicting electricity generation, as well as its corresponding price, are very challenging tasks that can support decision-making for the market agents. In addition to wind and hydraulic generation, photovoltaic energy has attracted a lot of contracts signed in this market. Therefore, this project aims to study the…
Machine Learning (ML) has been a very attractive tool to support economic plans and decision-making of companies, investors and public agents of electricity sector. In this context, an emerging research topic is the energy consumption derived from virtual currency mining, i.e., the so-called cryptocurrency mining. Such an issue has caused concern among governments and other players of ene…
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Although Remote Sensing (RS) images have been successfully used in several applications, the quality of the processed images may be dramatically reduced due to particular issues occurred in the data acquisition step, such as changes in signal capture or sensor failures, leading to the emergence of occlusions within the images. These occlusions make post-processing tasks less effective in …
Our one-year sandwich PhD proposal aims at investigating part of the computational and theoretical studies in the field of data segmentation based on dense graphs. In synergy with the doctoral research currently in progress, which deals with spectral clustering applications encompassing hexahedral meshes and images, the visiting research is intended to explore new methodologies to manipul…