Ricardo Cerri
CV Lattes
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Universidade Federal de São Carlos (UFSCAR). Centro de Ciências Exatas e de Tecnologia (CCET) (Institutional affiliation for the last research proposal) Birthplace: Brazil
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)
- IEEE World Congress on Computational Intelligence (WCCI 2018), AR.BR
- Multi-objective evolutionary methods for hierarchical and multi-label classification, AP.R SPRINT
Abstract
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 i...
- Hierarchical classification of transposable elements using machine learning, AP.R
Abstract
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 evolutio...
(Only some records are available in English at this moment)
- Multi-label classification in data-streams, BP.MS
Abstract
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 ove...
- Multi-label machine learning for protein subcellular localization, BP.IC
Abstract
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 m...
- Hierarchical classification of transposable elements and protein functions making use of machine learning, BP.IC
Abstract
Proteins are macromolecules responsible for almost every task necessary for the maintenance of cells, playing an essential role in the behavior and regulation of organisms. Advances in Molecular Biology have allowed an almost complete listing of the proteins that make up the organisms. However, there are a large number of proteins whose function is still unknown, opening space for a new...
(Only some records are available in English at this moment)
- Multi-output learning for data streams classification, BE.PQ
Abstract
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 r...
- Evaluation of novelty detection algorithms for multi-label data streams classification, BE.EP.MS
Abstract
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 ...
- Active learning for protein subcellular localization, BE.EP.IC
Abstract
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 reside at the same time in two or more locations in a cell or move between locations, usually supervised multi-label classification (MLC) methods are designed to attack this proble...
(Only some records are available in English at this moment)
| 4 / 4 | Completed research grants |
| 7 / 6 | Completed scholarships in Brazil |
| 6 / 6 | Completed scholarships abroad |
| 17 / 16 | All research grants and scholarships |
| Associated processes | |
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 4 |
| Citations | 16 |
| Cit./Article | 4.0 |
| Data from Web of Science | |
NAKANO, FELIPE KENJI; CERRI, RICARDO; VENS, CELINE. Active learning for hierarchical multi-label classification. DATA MINING AND KNOWLEDGE DISCOVERY, v. 34, n. 5, SI, JUL 2020. Web of Science Citations: 0. (16/12489-2, 17/19264-9)
CERRI, RICARDO; BARROS, RODRIGO C.; DE CARVALHO, ANDRE C. P. L. F.; JIN, YAOCHU. Reduction strategies for hierarchical multi-label classification in protein function prediction. BMC Bioinformatics, v. 17, SEP 15 2016. Web of Science Citations: 12. (15/14300-1)
SCHIETGAT, LEANDER; VENS, CELINE; CERRI, RICARDO; FISCHER, CARLOS N.; COSTA, EDUARDO; RAMON, JAN; CARARETO, CLAUDIA M. A.; BLOCKEEL, HENDRIK. A machine learning based framework to identify and classify long terminal repeat retrotransposons. PLOS COMPUTATIONAL BIOLOGY, v. 14, n. 4, APR 2018. Web of Science Citations: 3. (15/14300-1, 12/24774-2, 13/15070-4)
CERRI, RICARDO; BASGALUPP, MARCIO P.; BARROS, RODRIGO C.; DE CARVALHO, ANDRE C. P. L. F.. Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms. APPLIED SOFT COMPUTING, v. 77, p. 584-604, APR 2019. Web of Science Citations: 1. (15/14300-1, 16/50457-5)
(References retrieved automatically from State of São Paulo Research Institutions)
CERRI, Ricardo. Redes neurais e algoritmos genéticos para problemas de classificação hierárquica multirrótulo. Tese (Doutorado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos. (09/17401-2)
CERRI, Ricardo. Técnicas de classificação hierárquica multirrótulo. Dissertação (Mestrado) - Instituto de Ciências Matemáticas e de Computação. Universidade de São Paulo (USP). São Carlos. (08/01999-3)