CV Lattes
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Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Instituto de Geociências e Ciências Exatas (IGCE) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Graduated in Computer Science (Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP, 2005), Masters in Computer Science (University of Campinas - UNICAMP, 2008) and a PhD in Computer Science (University of Campinas - UNICAMP, 2012 ). He is currently Associate Professor at the Universidade Estadual Paulista Júlio de Mesquita Filho - UNESP, Department of Statistics, Applied Mathematics and Computation and associate researcher at the Institute of Computing - UNICAMP. He has experience in various areas of computer science, with special interest in digital libraries, image processing and analysis, information retrieval, image retrieval by content and high performance computing. (Source: Lattes Curriculum)
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Large Language Models (LLMs) have emerged as universal models for language understanding and inference. In order to operate factually, these models need to be provided with a relevant associated factual context. Retrieval Augmented Generation (RAG) became the complementary architectural mechanism which enables LLMs to operate factually, in which a retrieval step delivers the associated fa…
Natural Language Processing (NLP) refers to a branch of artificial intelligence (AI) concerned with giving computers the ability of interpreting and reasoning over human languages. Intrinsically multidisciplinary, it involves integrates diverse research areas, such as linguistics, logics, computer science, and artificial intelligence. In a world mainly guided by technology and increasingl…
Unsupervised Learning methods have been established as a solution to increase the effectiveness of content-based searches without requiring user intervention. These methods exploit contextual relationship among images, usually encoded in the distance/similarity information of the collections.This research project intends to investigate the application of such methods in new and diversifie…
Several machine learning techniques have relied on large labeled data sets to construct predictive models and solving supervised learning tasks. The use of deep learning techniques can be highlighted, since it have been broadly and successfully used in various domains. On the other hand, in many circumstances, the labeled sets are unavailable or insufficient to train effective supervised …
Content-Based Image Retrieval (CBIR) systems aims at retrieving the most similar images in a collection by taking into account image visual properties. Users are interested in the images placed at the first positions of the returned ranked lists, which usually are the most relevant ones. Therefore, accurately ranking collection images is of great relevance. However, in general, CBIR appro…
Considering the huge challenges posed by the complexity of language aspects to the development of AI-based algorithms capable of comprehending and grasping a language, the emergence of LLMs represents a significant advancement. However, despite the significant advancements and the large spectrum of applications, LLMs face challenges, with important associated limitations. Especially in ha…
Climate change is making natural disasters more frequent. Floods, hurri-canes, and earthquakes are becoming more intense and frequent in several coun-tries. In this scenario, it is essential to have rescue teams ready to save potentialvictims of accidents, building collapses, and landslides. For these teams, it isvital to be ready for what might be found in destroyed areas. Robots like dr…
Despite the significant progress made in supervised learning, the common requirement for extensive labeled datasets represents a severe bottleneck. In this scenario, other learning paradigms capable of addressing the challenge associated with the scarcity of labeled data represent a relevant alternative solution. Self-Supervised Learning (SSL) approaches have been established as a promisi…
In an unsupervised scenario, where no labeled data is available, part of the problem of improving retrieval results consis in identifying high and low effective ranked lists. Therefore, estimating the effectiveness of retrieved results without the need of user intervention is of critical importance. The quality of retrieved results for a given query may be used to improve the search syste…
Noise consists of a degradation inherent in the acquisition process, in the most diverse types of imaging. In medical imaging, for example, this may impair or hamper image analysis by the radiologist. In addition, in the case of imaging systems employing ionizing radiation, such as Computed Tomography (CT), Mammography or Mammary Tomosynthesis, where the search for dose reduction should b…
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With the wide applicability of time series data in various domains, including medicine, agriculture, economics, and science, the analysis and processing of such data have become increasingly demanding. Information Retrieval tasks applied to time series data play a crucial role in identifying patterns and ranking data based on similarity, making them a key focus in the area. Machine Learni…
| 2 / 2 | Ongoing grants |
| 4 / 3 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 17 / 17 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
| 27 / 26 | All research grants and scholarships |
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