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 60 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)
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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…
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…
(Only some records are available in English at this moment)
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…
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…
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)
In medical settings, data can be collected from heterogeneous sources, such as X-ray machines, blood exams, wearables, and Internet of Things devices. Artificial Intelligence (AI) strategies, such as Deep Learning (DL), can combine these data to discover patterns and anomalies. DL is specialized in classifying medical images to assist physicians in diagnosis. Even though AI can enhance th…
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 …
(Only some records are available in English at this moment)
| 16 / 12 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 42 / 25 | Completed scholarships in Brazil |
| 6 / 5 | Completed scholarships abroad |
| 65 / 43 | All research grants and scholarships |
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