CV Lattes
Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação (FEEC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
She received a Bachelor's degree in Electrical Engineering from São Paulo State University, UNESP, (2006) and a Master's degree in Electrical Engineering from the University of Campinas, UNICAMP, (2009). She concluded her PhD thesis in co-tutorship between the University of Nice-Sophia Antipolis, France and UNICAMP (2014). Her thesis work was done in collaboration between the laboratory of Computer Science, Signals and Systems (I3S), supervised by Dr. Gérard Favier, and the laboratory of Signal Processing for Communications (DSPCom), supervised by Prof. Dr. João Marcos T. Romano. She is currently a postdoc researcher at RIKEN Brain Science Institute in Wako/Japan. Her areas of interest include: Tensorial decompositions and applications; Signal processing; Big data processing/analysis; Deep learning; Source separation; Blind and semi-blind equalization; Space-Time-Frequency coding; Tensorial approaches for seismic signal processing. (Source: Lattes Curriculum)
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Several recent research topics related to the information processing are characterized by an increasing amount of data. As a consequence of this increase, the processing of these data tends to require more sophisticated methods than those classically employed in matrix approaches. Thus, the tensor approach is becoming a natural and powerful tool in this context, as it ensures that all dat…
Deep learning neural networks have attracted the attention of the machine learning community because of their appealing data-driven framework and of their performance in a number of pattern recognition tasks. It is well known that the state-of-the-art Deep Neural Networks (DNNs) are highly redundant and contain hundreds of millions of parameters, using up all available memory of personal …
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