CV Lattes
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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
He holds a PhD in Computer Science and Computational Mathematics from the Institute of Mathematics and Computing Sciences of the University of São Paulo (ICMC/USP), a master's degree also from ICMC/USP and a degree in Information Systems from the Federal University of Mato Grosso do Sul. He did doctoral research internship at the École Nationale Supérieure de Techniques Avancées (ENSTA, Institute Polytechnique de Paris) in France. He held a Post-Doctorate in Computational Physics at the Institute of Physics of São Carlos at USP (IFSC / USP). Currently, he is Assistant Professor at the Institute of Biosciences, Letters and Exact Sciences (IBILCE) at the São Paulo State University (UNESP). He is part of or has been part of research projects on machine learning, computer vision and complex networks.His doctoral thesis received several awards: Honorable Mention in the CAPES Thesis Award (2022); Honorable Mention in the USP Highlight Thesis Award (2022); 3rd place in the XXXV Theses and Dissertations Award of the Brazilian Computing Society - SBC (2022). He has 24 publications in international journals and 19 in conferences. He is a reviewer for several international journals and conferences. He is interested in the research fields of Computer Vision, Mobile Computing, Pattern Recognition, Machine Learning, Artificial Neural Networks, Complex networks and applications. (Source: Lattes Curriculum)
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Pattern recognition in images is a key topic of computer vision, involving the analysis of visual features (such as texture, color, and shape) and having various applications. Currently, there are two main paradigms of methods, each with advantages and disadvantages: the classic, which uses manual descriptors and requires greater domain knowledge, and approaches based on machine learning …
Textures are essential attributes in pattern recognition in images and have practical applications in areas such as medicine, materials science, and botany. Although some textures present simple visual patterns, real textures, such as those found in nature, usually involve non-linear processes in their formation, which increases complexity and makes analysis difficult. In addition, many r…
With the increasing complexity and diversity of images captured by different devices, there is a need for more sophisticated image analysis methods. The images captured by biosensors are examples of contemporary data that are still little studied. They are composed of complex patterns that require highly discriminative methods, capable of describing their complexity to perform recognition…
Textures are essential attributes in pattern recognition in images and have practical applications in areas such as medicine, materials science and botany. Although some textures may present simple visual patterns, in general, real textures, such as those from sensors, involve non-linear processes in their formation, which adds complexity and makes analysis difficult. This project aims to…
Texture analysis is a classic yet still challenging task in computer vision, for which artificial neural networks are actively being applied. Most approaches take advantage of large and complex architectures of convolutional neural networks trained with huge databases and that demand high processing power. In contrast to these approaches based on complex architectures, Randomized Neural N…
Great efforts have been made by researchers from different areas to develop sensors and biosensors for various purposes such as early cancer diagnosis, detection of contamination in food/water, etc. In this sense, many researchers have focused on the development of new low-cost detection and diagnosis strategies. Particularly, promising results have been achieved using machine learning te…
Complex networks have been used as a study tool and contributed in several areas of science due to their interdisciplinary nature and conceptual simplicity. In computer science, networks have been used in problems of architecture, artificial intelligence, computer networks and computer vision. With regard to computational vision, several approaches have been proposed over the past decade …
Complex network is being increasingly used due to its flexibility and ability to represent and analyze any discrete system and many types of data. In this sense, pattern recognition and complex networks emerge as an important alternative in data science.Pattern recognition in networks aims the feature extraction to classify large-scale networks into several classes, instead of focusing o…
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| 8 / 8 | All research grants and scholarships |
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