CV Lattes
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Currently, he is an Associate Professor in the Department of Computer Science (CIC) at the University of Brasília (UnB). He obtained his PhD (2016) in Computer Science and Computational Mathematics from the Instituto de Ciências Matemáticas e de Computação (ICMC) at the Universidade de São Paulo (USP), in São Carlos-SP, with an internship abroad period (2014-2015) at the University of California, Davis (UC Davis), in Davis, California, United States. He earned his Master's degree (2011) and Bachelor's degree (2009) in Computer Science from the Faculdade de Computação at the Universidade Federal de Uberlândia. He was a Substitute Professor (2016-2017) in the Departamento de Ciência da Computação at the Universidade Federal de Lavras (UFLA), in Lavras-MG. His research areas include machine learning, natural language processing, data visualization, and computer vision. He was the coordinator of the extension project "Maratona de Programação no Distrito Federal" between 2019 and 2024, and was one of the coaches of the CIC/UnB teams in the SBC Programming Marathon and the International Collegiate Programming Contest (ICPC). He is a member of the Brazilian Computer Society and the IEEE. (Source: Lattes Curriculum)
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This Ph.D. project has the objective of employing Information Visualization and Multidimensional Visualization techniques to support biologists in tasks involved in the taxonomic classification of fresh water green algae. Taxonomical classification of such organisms is problematic due to their feature variations. Similar features may be identify multiple algae species, which introduces er…
The taxonomic classification of freshwater green microalgae is highly problematic, with many known inconsistencies. Biologists-taxonomists manually inspect and analyze microscopy images, and typically carry out several complex and meticulous procedures that are time consuming and demand considerable expert knowledge. The problem can greatly benefit from computational tools, in particular …
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