Associate Professor in the Department of Computer Science at the Federal University of São Carlos (UFSCar), Brazil, and a CNPq Research Productivity Fellow. Senior researcher in Computational Intelligence, leading a broad research portfolio spanning Data Science, Machine Learning, and Natural Language Processing, with recent funded projects addressing critical challenges in areas such as autonomous driving, healthcare, agriculture, and Industry 4.0. He serves on the editorial boards of leading journals, including Machine Learning and Data Mining and Knowledge Discovery (Springer), and has held key organizational roles in major conferences, most recently at the ECML-PKDD, serving as Journal Track Chair (2025) and Area Chair (2026). With over 100 peer-reviewed scientific publications in high-impact venues, he combines research excellence with strong educational engagement. He is the author of an award-winning textbook on Machine Learning, widely adopted in Brazil and Portugal for its clear treatment of both the mathematical foundations and practical applications of Artificial Intelligence.
Highlights
Winner of the Brazilian Jabuti Prize (the most important literary award in Brazil), the book Inteligência Artificial – Uma Abordagem de Aprendizado de Máquina addresses the main machine learning concepts and algorithms and points out ways to transform this knowledge into practical applications.
SMS Spam Collection is a public set of SMS-labeled messages collected for mobile phone spam research. It has one collection composed of 5,574 English, real, and non-encoded messages tagged as legitimate or spam.
MDLText is an efficient, lightweight, scalable, and fast multinomial text classifier. It exhibits fast incremental learning and is sufficiently robust to prevent overfitting, which are desirable features in real-world applications, large-scale problems, and online scenarios.
Federal University of São Carlos (UFSCar)
According to the Brazilian Ministry of Education, UFSCar is one of the best universities in the country. Its staff of researchers is one of the most productive in terms of articles published in indexed International Journals of Science, among all Brazilian universities.
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