Raphael Y. de Camargo is an Associate Professor at Federal University of ABC (UFABC), at the Center for Mathematics, Computing and Cognition. He holds a PhD in Computer Science from IME / USP, a Masters from IF / USP and a Bachelor of Science in Molecular Sciences from USP. He is currently permanent advisor in the Master's and Doctoral programs in Computer Science and Neuroscience and Cognition, at Federal University of ABC. He has experience and work in: (1) High performance computing (GPGPU, heterogeneous computing and scheduling), (2) Machine Learning (high performance computing, smart cities and neuroscience applications), and (3) Computational neuroscience (computational models of memory and time perception, connectivity analysis and decoding of neural signals). (Source: Lattes Curriculum)
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The Center for Innovation in Urban Public Policies, CITIES, is concerned with the spatial distribution of income where the most vulnerable populations live on the outskirts of cities far from jobs, studies and a series of complementary activities that influence their formation. This distribution of space ends up greatly reducing opportunities for the poorest populations. CITIES intends to…
Universidade Federal do ABC (UFABC) is one of the new Brazilian Federal universities, created on July 26, 2005. For this reason, the support of development agencies is indeed essential for performing high-level research. In this proposal, we present a project to use the institutional infrastructure parcel of the Center for Mathematics, Computation and Cognition (CMCC). This proposal focus…
With the advent of different classes of accelerators such as GPUs (Graphical Processing Units) and Intel MIC (Many Integrated Cores), heterogeneous clusters composed of different types of accelerators and processors became reality. We consider dedicated and shared clusters, the later composed of shared workstations distributed in several laboratories. The architectural differences between…
Deep Neural Networks have been successful in Brain-Machine Interfaces (BCIs) applications, where they are used to decode mental images from Electroencephalography (EEG) signals. However, each trained model works for a single individual and requires large amounts of data, which is time-consuming and expensive to collect. Using transfer learning, one can train a neural network on a set of s…
Brain-Machine Interfaces (BCIs) may help individuals with movement restrictions to control devices by processing electrical signals captured by electrodes using, for example, commands to move a cursor up/down or right/left. The use of neural networks, especially convolutional networks, has shown promising results when used with the time series of electrodes as input. One problem is that t…
Nowadays, the general-purpose computing on graphical processing units (GPGPU) technique is widely accepted as a new paradigm of High Performance Computing for improving the performance of simulations, such as large-scale neuronal simulations. In this work we will develop an algorithm for solving a large sets of linear systems, each representing a single neuron, using GPUs. We will incorpo…
| 1 / 1 | Ongoing research grants |
| 3 / 3 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 4 / 2 | Completed scholarships in Brazil |
| 9 / 7 | All research grants and scholarships |
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