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Agma Juci Machado Traina

CV Lattes ResearcherID ORCID


Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC)  (Institutional affiliation for 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 
Climate and environmental connections 
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Managing Complex Big Databases to Support Decision Making in Health Systems

Published in November 26, 2019 - FAPESP NewsAgency. Technical Session 5: Data Science & Machine Learning for Complex and Dynamical Models. Managing Complex Big Databases to Support Decision Making in Health Systems. One of the biggest challenges posed to the computer science field is integrating, organizing, and taking advantage of very large volumes of multi-modal data from diversified platforms for boosting decision-making processes. That is, how is it possible to use data from a variety of sources (patient’s exams, monitoring and treatment) to gather information from similar cases and build a better understanding of the case under analysis? Moreover, many times the data are not complete but cannot be discharged, as in the usual approach. In this talk, I will briefly discuss challenges and ongoing researches at the University of Sao Paulo that deal with such challenges in the context of health systems.
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