Assistant professor at the Computer Science Department in the Federal University of São Carlos, he is graduated in Computer Science and has an M.S. and Ph.D. degrees in Computer Science and Computational Mathematics, all by the Institute of Mathematics and Computer Science of the University of São Paulo. His principal research theme is Data Mining and its applications, with focus on large volumes of continuous sequential data (such as time series and digital signals). Besides, has experience with data preprocessing for Machine Learning applications, class imbalance, and classification of data streams. He is the coordinator of the research group on Data Mining and Applications (MIDAS, the acronym for the group's name in Portuguese), which includes undergrad and graduated students. (Source: Lattes Curriculum)
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With the enhancement and miniaturization of sensors capable of obtaining and transmitting various types of data, the Internet of Things has been notably gaining space in both scientific research and industry. Sports analysis is among a multitude of applications of such a technology. Specifically, sensors have aided the statistical analysis of the athletes' performances. This type of devic…
With the growing ubiquity of smartphones, smartwatches, and other devices capable of collecting data across time, applications that use time series as input, such as cardiac monitoring and activity recognition, have become increasingly popular. Thus, various techniques have been developed for Machine Learning applied to time series. However, the majority of those techniques were created f…
Due to the increasing collection of overtime observed data, time series are becoming a ubiquitous type of data in human beings' daily lives. This fact has also caused a significant increase in the number of techniques for time series classification in recent decades. In this scenario, when a researcher or developer needs a solution based on a classification model, there is a wide range of…
With the large volume of data collected continuously and on a daily basis in several applications, it is increasingly common to obtain and store time series without any type of annotation. The challenge of labeling such data is due to the high cost of manually aggregating any information to the collected data. This situation becomes even more significant in the domain of healthcare applic…
(Only some records are available in English at this moment)
Applications that collect large volumes of temporal data using sensors have emerged in different knowledge domains. One way to obtain knowledge about the data in time series context is by learning classification models. A particular case in this context occurs when data from only one class of the problem can be obtained or, because of specific interest in this class, this is the only one …
This project has the intention to propose machine learning techniques in temporal series for Photoplethysmogram data obtained by smarthpones and avaluate its suitability comparing it with tradicional exams for measurement of hemoglobin, bilirubin, oxygen saturation and leukocytes. First, data from voluntary pacients of the UFSCar's Univesitary Hospital will be obtained, they will give the…
With the rapid growth in the distribution of digital music, organizing and retrieving musical information through computational methods has become an increasingly relevant task. While the volume of data associated with music collections becomes larger and more diverse, providing a good experience for users becomes a more complex task. The classification of musical genres is an important t…
(Only some records are available in English at this moment)
Processing growing quantities of information to transform it into knowledge is one of the biggest challenges in the Computer Science related areas nowadays, for both the industry and the academy. For this reason, we may find a plethora of Machine Learning (ML) techniques and platforms. While it is possible to note that these tools cover a large range of application domains and data types,…
Dynamic Time Warping (DTW) is certainly the most relevant distance for time series analysis. The main issue with DTW is its computational time complexity, which is quadratic to the number of observations. Given the importance of DTW to time series mining, a large body of research has been proposed to accelerate DTW calculations. However, all the recent advances in speeding up DTW are conf…
Applications that involve signals interpretation are ubiquitous in human life. A common task in this type of application is the classification. This task involves a mapping f:X->Y where X is the space of complex objects, in this case digital signals, and Y represents the space of possible categories or classes for which each signal can be associated. In this project, we are interested in …
| 1 / 1 | Completed research grants |
| 4 / 4 | Ongoing scholarships in Brazil |
| 9 / 9 | Completed scholarships in Brazil |
| 3 / 3 | Completed scholarships abroad |
| 17 / 17 | All research grants and scholarships |
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