CV Lattes
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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
She has a Bachelor degree in Computer Science from the ICMSC-USP (1983), a Master degree in Computer Science and Computational Mathematics from the ICMSC-USP (1987), and a PhD in Computational Applied Physics from the IFQSC-USP (1991). She also spent a sabbatical leave at the Carnegie Mellon University (1998-2000) working on Multimedia Databases. She has been a Full Professor with the Department of Computer Science at ICMC-USP since 2008.She participated in and coordinated several national and international projects, integrating multidisciplinary teams of researchers and students. She has published more than 250 papers in selective journals and conferences, where she has received several awards for best paper, as well as mentored more than 50 graduate students. She participates in several editorial committees of journals and steering committees of conferences in her field.Her research interests include indexing and retrieval of complex data by content, similarity queries, data visualization, visual data mining, as well as image and video processing. Agma has been working in the integration of the results of her research lines with applications for the areas of medicine and agro-climatology, aiming at the development of applied computational systems. (Source: Lattes Curriculum)
| Articles published in Pesquisa FAPESP Magazine about the researcher: |
| Conexiones climáticas y ambientales |
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Today, almost every human activity generates and/or demands to store and process massive, diverse and complex data, either in scientific, academic, enterprise and even leisure pursuits - the scenario being called as the “big data era”. Health-related activities are in the core of those activities, as they produce big data as well as they can take advantage of the technological storm, impr…
This supplementary research project aims at proposing, exploring and developing new methods and algorithms to be used in decision-making processes for medical diagnosis and prognosis of patients in the context of COVID-19. These methods and algorithms will be instantiated in systems and applications that will be made available to the scientific community to support this decision-making pr…
(Only some records are available in English at this moment)
Information visualization strategies have been developed and applied to optimize the analysis of data of different types and from different domains. In many situations in the real world, visual metaphors represent elements of a domain (for example, people) and their interactions (for example, medical consultations, friendships, or scientific collaborations). Given these characteristics, t…
With the advent of big data, data is being produced and generated on a large scale, used by Machine Learning (ML) models to generate new knowledge. Several areas have benefited from big data and ML, one of them being healthcare, which can employ complex data such as images to assist medical experts in decision making. While these concepts are valuable for healthcare, they can lead to issu…
The goal of this project is to explore the use of selective visual attention models to identify the most important visual regions in an image and use this information to build feature extraction techniques that allow mitigating the semantic gap between human perception and computational description. These models can lead attention to specific places of interest in the image according to h…
Efficient and accurate indexing and retrieval of information organized in databases to answer queries requested by users are pillars of the data science and engineering processes. Modern computer systems need to deal with complex data (images, long texts, videos, time series, among other types of data), which require differentiated and customized treatment for each specific data type. Thi…
(Only some records are available in English at this moment)
The huge amount of videos made available online every day has created a demand for intelligent tools supporting basic multimedia systems needs as indexing, retrieval and automatic tagging. Robust event recognition strategy can directly aid such needs. A common approach for this task is to extract low-level features from the videos and to use classical machine learning techniques to model …
In the last years, due to advances in technology, both the amount and variety of data available for analysis have significantly increased. This scenario has intensified the development of strategies, for fast and automatic processing of such data. Metric access methods (MAMs) are strategic for these purposes, because they aim at indexing the data, bringing effective ways for accelerating …
Social media services such as Facebook, Flickr and Google+ allow a single user to share multiple images, videos and comments. This generates a large volume of data, presenting a great challenging for information retrieval tasks. Unlike conventional data as numbers and short strings, for images and videos make no sense to compare them in their original domain. This kind of data is known as…
(Only some records are available in English at this moment)
| 1 / 1 | Ongoing research grants |
| 15 / 11 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 40 / 23 | Completed scholarships in Brazil |
| 5 / 4 | Completed scholarships abroad |
| 64 / 42 | All research grants and scholarships |
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