CV Lattes
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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Associate Professor at the Institute of Mathematics and Computer Sciences of the University of São Paulo (ICMC-USP). He holds a Bachelor's degree in Computer Science and a Master's and Ph.D. in Computer Science and Computational Mathematics, all from ICMC-USP, with research internships at Columbia University, NY, and the University of California, Riverside, CA. His main research area is Machine Learning and its applications, with a stronger focus on tasks involving large volumes of continuous sequential data (such as time series and digital signals). In addition, he has experience working with various types of data, including text and images, with particular interest in multimodal learning. (Source: Lattes Curriculum)
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Time series has become a fundamental data type in many applications, given the increasing pervasiveness of devices capable of collecting and storing temporal data. The most notable recent advances in machine learning for time series lie in the proposal of deep learning architectures. However, these neural networks usually rely on an enormous number of parameters, which makes them computat…
Monitoring physiological signs, vital signs, and other parameters that can be collected over time from individuals are essential in several tasks in healthcare, such as heart rate estimation and the identification of abnormal heartbeats. However, these time series are obtained by very expensive and usually not portable equipment. On the other hand, with the improvement and miniaturization…
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…
This research proposes developing and evaluating deep neural networks for analyzing time series in healthcare applications, focusing on data from real-world data repositories such as PhysioBank. Initially, experimental studies will be conducted to compare classification and extrinsic regression algorithms, identifying data standardization guidelines and assessing their advantages and limi…
Deep neural networks require high computational costs, especially in mHealth applications with limited hardware. The project proposes to explore knowledge distillation in time series classification tasks. This technique involves transferring the learning from a larger network (teacher) to a smaller one (student), which is trained to reproduce similar outputs more efficiently. This approac…
The monitoring of physiological signals, vital signs, and other parameters collected over time is essential in various tasks in the healthcare field, such as estimating heart rate and identifying anomalous heartbeats. However, these time series are typically obtained through costly and non-portable equipment. On the other hand, with the improvement and miniaturization of sensors capable o…
Music plays a fundamental role in human life. With the evolution of production and dissemination technologies, especially streaming platforms, the music market has grown significantly. To improve user experience on these platforms, it is essential to create tools that rely on both the characteristics of the music and its consumers. Recommender systems are crucial to this experience, but t…
Monitoring physiological signals, vital signs, and other parameters that can be collected over time is essential in various healthcare tasks, such as estimating heart rate and identifying abnormal heartbeats. However, costly and usually non-portable equipment is the usual means to obtain these time series. On the other hand, with the improvement and miniaturization of sensors capable of t…
With the growing interest in time series in recent decades, which has been intensifying in recent years, researchers have proposed dozens of specific algorithms for classifying and forecasting time series. However, there is still a great demand for techniques associated with the extrinsic regression of time series. By "extrinsic," it is understood that the target of the prediction is an e…
(Only some records are available in English at this moment)
Music plays a fundamental role in human daily life. With the evolution of production and distribution technologies, especially streaming platforms, the music industry has grown significantly. To enhance user experience on these platforms, it is essential to develop tools that leverage both the characteristics of the music and the preferences of consumers. Recommendation systems are crucia…
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. Several of these scenarios can be mapped naturally as tasks involving multivariate time series. Thus, various techniques for Machine Learning appl…
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. Several of these scenarios can be mapped naturally as tasks involving multivariate time series. Thus, various techniques for Machine Learning appl…
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 for Machine Learning applied to time series have been developed. However, most of those techniques were created for class…
(Only some records are available in English at this moment)
| 2 / 2 | Ongoing grants |
| 1 / 1 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 20 / 20 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 8 / 8 | Completed scholarships abroad |
| 35 / 35 | All research grants and scholarships |
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