CV Lattes
Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Bachelor's Degree in Computer Science from the Federal University of Mato Grosso (2013), MSc (2015) and PhD (2019) in Computer Science and Computational Mathematics (PPg-CCMC - CAPES 7) from the University of São Paulo. During his PhD, he held a Research Internship abroad (BEPE/FAPESP) as Visiting Research at the Department of Computing Science of the University of Alberta under the supervision of Prof. Dr. Jörg Sander (Sep/2017 - Aug/2018), where he returned after finishing his PhD for a postdoc (Jan/2020 - Feb/2021). Subsequently, he pursued a postdoctoral position at the University of Southern Denmark (SDU), under the supervision of Prof. Dr. Arthur Zimek. Since his Undergraduate (Scientific Initiation) he has been interested in and worked in the areas of Data Mining and Machine Learning, having worked directly with Pattern Classification, Regression Analysis, Cluster Analysis, Outlier Detection, One-Class Classification and Intrinsic Dimensionality. (Source: Lattes Curriculum)
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Outlier detection plays an important role in the pattern discovery from data that can be considered exceptional in some sense. Detecting such patterns is relevant in general because in many data mining applications, such patterns represent extraordinary behaviors that is worth further analysis. An important distinction is that between the supervised and unsupervised techniques. In this pr…
Although there is a growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results has been notably overlooked. In contrast to the unsupervised cluster analysis, where indexes for internal evaluation and validation of clustering solutions have been conceived and shown to be very useful, in the outlier detection domain o…
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