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Universidade Federal de São Carlos (UFSCAR). Centro de Ciências Exatas e de Tecnologia (CCET) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Alexandre L. M. Levada received the B.S. degree in computer science from the São Paulo State University (UNESP) in 2002. He completed his M.Sc degree in Computer Science from Federal University of São Carlos (UFSCar) in 2006 and his Ph.D degree in Computational Physics from University of São Paulo (USP) in 2010. In the same year, he joined the Computing Department at Federal University of São Carlos. From 2010 to 2024 he was an assistant professor with the Computing Department, where he has taught several undergraduate and graduate courses as mathematical logic, graph theory, discrete mathematics, digital image processing, signals and systems, pattern recognition, computer vision, algorithms and data structures, design and analysis of algorithms, scientific programming and mathematical optimization. Since 2024, he has been an associate professor with the same department. His main interest research topics include signal and image denoising, pattern recognition and machine learning with emphasis on graph-based classification algorithms, information-theoretic dimensionality reduction methods and the application of information geometry in the analysis of random field dynamics. He is the author of one book, more than 25 journal papers and more than 55 conference papers. He was awarded the best scientific paper award in the track Artificial intelligence, machine learning and pattern analysis for a paper presented at the 26th International Conference on Pattern Recognition (ICPR) in 2022. He was the recipient of a CNPq (National Council for Scientific and Technological Development) research productivity grant in 2025. (Source: Lattes Curriculum)
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Data clustering is a fundamental task in many areas of computer science and engineering, with applications in pattern recognition, data mining, image processing, among others. In recent years, graph-based approaches have gained prominence due to their ability to model complex relationships between data. Furthermore, distance metrics based on Information Theory have been widely used to mea…
Image denoising is a topic of great interest in the areas of image processing and computer vision, especially in medical and biological applications. The most common degradation model in image noise reduction is the Gaussian noise, in which the noise is additive and independent of the signal of interest, which simplifies the derivation of numerical methods and algorithms. However, due to …
Data clustering is a fundamental technique in the field of machine learning and data analysis. Its importance lies in the fact that it is an unsupervised approach that allows identifying patterns and hidden structures in data, without the need for labels or prior information about the classes. One of the best-known algorithms for this purpose is K-means. Despite being widely used, it has …
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