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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
Rodrigo Fernandes de Mello is Associate Professor at the Institute of Mathematical and Computer Sciences, Department of Computer Science, University of São Paulo, São Carlos, SP. He is author of the book Machine Learning: A Practical Approach to the Statistical Learning Theory, and he has been dedicated to the theoretical development and its practical connections in the Machine Learning area, investigating learning bounds for supervised and unsupervised algorithms. He counts on previous experience in the industry before assuming his current position in the academia, which brings his attention to practice, implementation of algorithms and solutions. He obtained his doctorate in November 2003 from the School of Engineering at the University of São Paulo, São Carlos, SP, Brazil and has a master's degree in Computer Science from the Federal University of São Carlos, Brazil. His research interests include theoretical and generalization aspects for Supervised and Unsupervised Machine Learning, Time Series Analysis with Statistical and Dynamic Systems tools, in addition to modeling Online Learning systems and Data streams. (Source: Lattes Curriculum)
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Several industrial, scientific and commercial processes produce data continuously over time, in large volumes and at high frequencies, called data streams. In the Machine Learning area, researchers have been modeling and analyzing the behaviour of these streams in attempt to understand the different phenomena. In many scenarios, data streams have their behaviour changed over time, which i…
Most of the current Grid management systems focus the development and application scheduling. In such context, those environments could evolve in optimization aspects by considering application knowledge. Most of the management systems still require application rewriting. Those limitations motivated the MidHPC (Middleware for High Performance Computing) project which provides a middleware…
(Only some records are available in English at this moment)
Machine learning has confirmed its usefulness to various problems, from batch driven applications to time series forecasting (e.g., stock prices, weather, energy consumption, behavior of bacteria, etc). Amid a plethora of existing empirical results regarding time series, this project intends to provide theoretical evidences that the Statistical Learning Theory (SLT) is sufficient to ensur…
The increase in the amount of data coming from different sources has lead to more complex data analysis and processing, making it difficult to to find patterns, trends and cycles. Among the types of analyzed data, there is special attention to those collected over time, commonly arranged in the form of time series. These data can describe different phenomena, whether natural or produced b…
With technological developments, it has become possible to collect and model large amounts of data produced over time. These data are typically generated by industrial processes, human operations or natural phenomena. By modeling them, we can understand, predict and observe their changes as well as control them. Overall, these data are formed by deterministic and stochastic components. Wh…
(Only some records are available in English at this moment)
Data analysis has been motivating new methods to find patterns, clusters and outliers in different scenarios and conditions. In the scope of data evolving along time, also referred to as time series or data streams, solutions derived from areas such as Dynamical Systems and Statistics (DSS) have gained special attention in the literature. However, the DSS framework is usually not applied/…
With the objective of modeling data stream changes, several researchers have been designing new approaches to detect concept drifts. A concept is characterized by a sequence of observations produced by a same generating process. Researchers are interested in detecting concept drift in order to support specialists to make decisions on the phenomena that generated such streams. Currently, t…
Several systems or other phenomena produce outputs, i.e., results, which can be collected over time. Phenomena related to medicine are of particular interest due to they affect people's quality of life. Among such phenomena, there is interest in understanding under which conditions the cancer disease occurs and what are its relationships to other factors or variables such as habits, geogr…
| 8 / 6 | Completed research grants |
| 18 / 14 | Completed scholarships in Brazil |
| 3 / 3 | Completed scholarships abroad |
| 30 / 24 | All research grants and scholarships |
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