CV Lattes
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Universidade Federal do ABC (UFABC). Centro de Matemática, Computação e Cognição (CMCC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
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…
Public transportation users often need to employ some alternate form of transportation to cover the short distances before they reach the nearest mass transit system. This movement, referred to as the "last mile," is typically costly and inefficient. An emerging alternative involves the use of vans (microtransit), which originate from or travel to high-capacity and medium-capacity transpo…
Automated classification of sleep stages is a complex task, arising from the high-dimensionality and low signal-to-noise ratio of electroencephalogram (EEG) signals, in addition to the high variability among individuals. Although deep neural networks, including convolutional architectures and Transformers, have shown promising results for automated classification, training these models st…
The process of classifying sleep stages is time-consuming and intensive and requires experts to analyze hours of EEG signals. The use of deep neural networks has been generating promising results, especially with convolutional architectures. These networks receive 30s windows as input and classify the sleep stage at that moment. However, contextual information, such as sleep stages at ear…
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…
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The evolving landscape of urban mobility has witnessed the advent of Mobility-on-Demand (MoD) systems, including ride-hailing services. There is increasing interest in applying this concept to public transportation, for instance, for first- and last-mile connectivity to public transportation hubs through microtransit solutions like on-demand vans. However, dynamically managing these syste…
Riemannian Alignment (RA) techniques are considered state-of-the-art methods for transfer learning between subjects in the brain-computer interface (BCI) motor imagery paradigm. In the current fellowship, we showed that Euclidean alignment (EA) techniques enable efficient transfer learning between subjects using Deep Learning models. In this internship project, we will compare transfer le…
| 1 / 1 | Ongoing grants |
| 3 / 3 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 7 / 5 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 1 / 1 | Completed scholarships abroad |
| 14 / 12 | All research grants and scholarships |
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