CV Lattes
ResearcherID
ORCID
Google Scholar Citations
Universidade Federal de São Carlos (UFSCAR). Centro de Ciências Exatas e de Tecnologia (CCET) (Institutional affiliation from the last research proposal) Birthplace: Brazil
He holds a Bachelor's degree in Computer Science from the University of São Paulo (2004), a Master's degree in Computer Science and Computational Mathematics from the University of São Paulo (2006), and a Ph.D. in Computer Science and Computational Mathematics from the University of São Paulo (2011). Between 2020 and 2022, he was on sabbatical, as a researcher / visiting professor, at the Department of Computer Science at the University of Alberta, in Edmonton, AB, Canada. Is a member of several research projects funded by national and state Brazilian funding agencies. He is currently an Associate Professor at the Federal University of São Carlos and is part of the advisory body of the Graduate Program in Computer Science at the Department of Computing. Has experience in Computer Science, with emphasis on Data Mining, working mainly on the following topics: Machine Learning, Data Grouping, Parallel, Distributed, and Evolutionary Computing. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| More itemsLess items |
| TITULO |
| Articles published in other media outlets ( ): |
| More itemsLess items |
| VEICULO: TITULO (DATA) |
| VEICULO: TITULO (DATA) |
The increasing amount of data generated by today's technologies makes its analysis challenging. First, because much of this data is often not identified (labeled) during its creation and therefore the organization/relationship between its objects are not explicit. Second, it is necessary that the methods used for this analysis are scalable enough to reach their goals, even with the increa…
Finding the K Nearest Neighbors Graph (kNNG) of a dataset is an essential estimation used in several algorithms, such as clustering and anomaly detection. However, this operation is costly since when calculating all k neighbors of all points in the set, there is a quadratic complexity concerning the size of the data set. Thus, there is a physical limitation in the execution of these algor…
Clustering is an unsupervised machine-learning task essential to several applications. In this way, some algorithms need the k Nearest Neighbors Graph (k NNG) as part of clustering. However, finding the k NNG may be a computationally costly operation since calculating all k neighbors of all points in the set is needed, resulting in a quadratic complexity concerning the size of a data set.…
| 1 / 1 | Completed research grants |
| 4 / 1 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
| 6 / 3 | All research grants and scholarships |
| Associated processes | |