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Unsupervised Learning
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Daniel Carlos Guimarães Pedronette
Full Professor
Computer Science
Department of Statistics, Applied Mathematics and Computing (DEMAC)
São Paulo State University (UNESP)
Education
2009-2012: PhD in Computer Science, Institute of Computing, UNICAMP
2006-2008: MsC in Computer Science, Institute of Computing, UNICAMP
2001-2005: BSc in Computer Science, DEMAC, UNESP
Short Bio (in english) He is currently a Full Professor (MS-6) at the Department of Statistics, Applied Mathematics and Computing (DEMAC) of São Paulo State University (UNESP). He holds a B.Sc. in Computer Science (UNESP, 2005) and an M.Sc. and Ph.D. in Computer Science (UNICAMP, 2008 and 2012). In 2019, he earned his Habilitation (Livre-Docência) in Digital Image Processing. He is an Associate Editor of the journal Pattern Recognition (Elsevier) and served as Deputy Coordinator of the Graduate Program in Computer Science at UNESP. He has taken part in several national and international research projects. He was a visiting professor at Bangor University (UK), under a project funded by the Royal Academy of Engineering, and at Temple University (USA), under a program funded by the Fulbright Commission, as well as a Collaborating Researcher at the Institute of Computing of UNICAMP. He has coordinated research projects funded by CNPq (Universal) and FAPESP (Young Investigator I and II, PITE Microsoft, and a project in collaboration with the Swiss National Science Foundation SNSF). He coordinates and participates in Research and Development projects in the oil and gas domain, in cooperation with Petrobras. He works as a Researcher at the Artificial Intelligence Laboratory and as Deputy Leader of UNESPetro (Center for Applied Natural Sciences). He has experience and conducts research in several areas of Computer Science: information retrieval, image processing and analysis, machine learning, high-performance computing, and natural language processing. His research has resulted in the publication of more than 140 scientific articles in journals and conferences with selective editorial policies, and has been recognized through awards and nominations in national and international competitions and events. Biografia (in portuguese) Iniciou a carreira na Área de Informática em 1997. Concluiu o Curso Técnico em Informática em 2000 (Colégio Técnico de Limeira - UNICAMP). Possui Graduação em Ciência da Computação (Universidade Estadual Paulista - UNESP Rio Claro, 2005), Mestrado em Ciência da Computação (Universidade Estadual de Campinas- UNICAMP, 2008) e Doutorado em Ciência da Computação (Universidade Estadual de Campinas- UNICAMP, 2012).
Atualmente é Professor Associado na Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP, no Departamento de Estatística, Matemática Aplicada e Computação e pesquisador colaborador do Recod Lab, Instituto de Computação - UNICAMP. Tem experiência em diversas áreas da Ciência da Computação: sistemas de informação, bibliotecas digitais, processamento e análise de imagens, recuperação de informações, computação de alto desempenho e sistemas de recuperação de imagens por conteúdo. Realiza pesquisa em várias dessas áreas, em especial em temas relacionados à recuperação de imagens por conteúdo. Participa de vários projetos de pesquisa e é coordenador de um projeto Jovem Pesquisador - FAPESP. O trabalho de pesquisa do seu doutorado foi reconhecido em diversos prêmios. A pesquisa desenvolvida na UNESP também tem dado origem a premiações como a indicação como finalista ao prêmio de best paper da conferência ICIP 2014 e prêmio de best paper da conferência SIBGRAPI 2016.
Curriculum Vitae
Main Research Interests
1. Information Retrieval
1.1. Content-Based Image Retrieval (CBIR)
1.2. Re-Ranking & Rank Aggregation
1.3. Multimedia Retrieval
1.4. Textual and Multimodal Retrieval
2. Machine Learning
2.1. Unsupervised Distance Learning
2.2. Manifold Learning
2.3. Semi-Supervised Learning
3. Image Processing
3.1. Visual Descriptors
3.2. Shape Matching
4. Digital Libraries
4.1. SOA
4.2. Recommendation Services
5. Parallel Computing
5.1. Parallel Programming
5.2. Heterogeneous Computing

