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Ricardo Cerri

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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)

Research grants
Scholarships in Brazil
Scholarships abroad
FAPESP support in numbers * Updated October 17, 2020
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Keywords used by the researcher
Scientific publications resulting from Research Grants and Scholarships under the grantee's responsibility (4)

(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)

Publications4
Citations16
Cit./Article4.0
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, . 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, . 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, . 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, . Web of Science Citations: 1. (15/14300-1, 16/50457-5)

Academic Publications

(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

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