Prof. Dr. Ricardo Cerri obtained his P.hD in Computer Science and Computational Mathematics from University of São Paulo (ICMC/USP/Brazil), working with Neural Networks and Genetic Algorithms for Hierarchical and Multi-label Problems. During his P.hD., he had a long research stay at Universities of Surrey and Kent, both in United Kingdom. His P.hD Thesis was the 2nd Best PhD Thesis in the Brazilian Contest of Artificial Intelligence Thesis promoted by the Brazilian Computer Society (2014). He has experience working mainly with Bioinformatics and Machine Learning, focusing on advanced methods for data classification and regression, such as multi-output and structured learning (multi-label, hierarchical and multi-target). Currently he holds the position of Assistant Professor at the Department of Computer Science from Federal University of São Carlos (UFSCar/Brazil), leading the Bioinformatics and Machine Learning research group (BioMal - http://www.biomal.ufscar.br/), supervising both bachelor and graduate students. He is also a researcher on Productivity at CNPq - Level 2. Dr. Cerri has ongoing international collaborations with partners from University of Kent (UK), University of Surrey (UK), University of Nottingham (UK), University of Porto (Portugal), and Katholieke Universiteit Leuven (Belgium), besides many Brazilian collaborations. Acts as reviewer for international and Brazilian journals and conferences, and acts in the program committee of different Brazilian and foreign conferences and workshops. From 2019 to 2020, Dr. Cerri conducted a postdoctoral research at the Laboratory of Artificial Intelligence and Decision Support (LIAAD) in University of Porto (Portugal), working with multi-output learning in data streams. (Source: Lattes Curriculum)
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The aim of this project is to provide a series of molecular markers and candidate genes, both associated with grain yield to assist in the development of new commercial rice strains and cultivars. For this, the project will use two databases from Embrapa's Nuclear Rice Collection, one phenotypic, generated from a series of nine field experiments where its 550 accessions were evaluated for…
In conventional classification, an instance is classified in just one among two or more classes. These problems are called single-label classification problems. However, there are more complex problems in which an instance can be classified in two or more classes simultaneously. These are known in the Machine Learning literature as multi-label classification problems. When the classes inv…
Transposable Elements (TEs) are DNA sequences which can move from one place to another inside the genome of a cell. These elements contribute to the genetic diversity of species, and their transposition mechanisms may affect the functionality of genes. The correct identification and classification of these elements is useful for the comprehension of their effects in the genomes evolutiona…
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Single nucleotide polymorphisms (SNPs) are variations at unique positions in the nucleotide sequence where the DNA is created. Due to the genetic alteration, SNPs are extremely important for human health studies. By them, it is possible to predict the individual response to some particular medications, for example. Furthermore, SNPs can be associated with more complex diseases, like cardi…
Data Streams are unlimited data sequences, continuously generated, non-stationary, and in many cases arriving in high speed. Novelty Detection is a classification task that verifies if an example, or a set of examples, differs significantly from the previously seen examples. This is an important task for Data Stream Classification, since new concepts may appear, disappear, or evolve over …
Protein subcellular localization is a really important classification task, because the location of proteins inside a cell is directly related to these protein's functions. As there are a lot of proteins that resides at the same time in two or more locations in a cell or move between locations, this can be an arduous task. In Machine Learning, this kind of classification task is named mul…
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Single Nucleotide Polymorphism (SNP) is the variation at a single position of a nucleotide chain where the DNA is formed. The association of SNPs and phenotypes is pertinent to assess the genetic basis of human health and the investigation of the genomes. The information provided by SNPs enables predicting the responses of an individual to certain drugs, as well as for the search of genes…
Data streams (DS) are unlimited, continuously generated, non-stationary, and in many cases high-speed data. Many real-world applications generate large amounts of data in a continuous stream, and with the evolution of Information Technology, more data will be generated and collected constantly. This highlights the relevance and necessity for developing algorithms capable of extracting rel…
Data streams (DS) are potentially unlimited sequences, generated continuously, non-stationary and, in many cases, at high speed. There are several studies currently investigating the application of Novelty Detection (ND) techniques in DS. This is because ND is an important task, mainly because new concepts may appear, disappear or evolve over time. Most of the work found in the novelty de…
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| 1 / 1 | Ongoing research grants |
| 4 / 4 | Completed research grants |
| 8 / 7 | Completed scholarships in Brazil |
| 7 / 7 | Completed scholarships abroad |
| 20 / 19 | All research grants and scholarships |
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