CV Lattes
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Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Instituto de Geociências e Ciências Exatas (IGCE) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Bachelor's degree in Computer Science from the São Paulo State University (UNESP) in Rio Claro, and Master's degree in Computer Science from the same institution. Honored for the best academic performance in the class during the undergraduate program. Investigates the machine learning and information retrieval fields since the second year of her undergraduate studies, conducting undergraduate research in clustering, manifold learning, classification, and weakly supervised learning, specifically in the field of images. During the Master's course, the research was focused on representation methods, information retrieval, and the application of machine learning techniques to time series. During the Master's program, a teaching internship was completed (PAADES) in the course of Algorithm Analysis, at São Paulo State University; in addition, taught the course "Operating Systems II", at São Paulo State University, for one semester and also completed a research internship at Wageningen University and Research in the Netherlands for two and a half months. Currently, works at São Paulo State University (UNESP) on a project titled 'Software development for automatic recognition of microfossils 1', funded by the company Petrobras. (Source: Lattes Curriculum)
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Many applications in science, e-commerce, and surveillance monitoring rely on recording sensor information in regular time intervals. As a consequence, time-series data has been growing tremendously. This flood of data requires data management for fast storage and access. Therefore, similarity search on time series data is a typical requirement for such databases. Besides the time series …
While traditional distance/similarity measures are mainly based on pairwise analysis, contextual measures also consider neighborhood similarity relationships. Recently, such measures have been successfully exploited in various unsupervised learning tasks, especially using graph-based approaches. In this scenario, this project considers the hypothesis that these measures can also be applie…
With the wide applicability of time series data in various domains, including medicine, agriculture, economics, and science, the analysis and processing of such data have become increasingly demanding. Information Retrieval tasks applied to time series data play a crucial role in identifying patterns and ranking data based on similarity, making them a key focus in the area. Machine Learni…
| 2 / 2 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
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