CURRENT ACTIVITIES

  • Associate Professor at Universidade Federal do ABC (UFABC), Brazil 

  • Vice-Director of the Center for Mathematics Computing and Cognition (CMCC)

  • Member of the Master's and Doctoral program in Computer Science

  • Member of the Master's and Doctoral program in Neuroscience and Cognition

CURRENT RESEARCH AREAS

My current main research areas are in High-Performance Computing, Machine Learning, and Computational Neuroscience. I work mostly at the intersection of the above areas, bridging the gap among them.

HIGH-PERFORMANCE COMPUTING

Learning better HPC Scheduling Policies

  • Existing supercomputers must process many thousands of jobs (programs) submitted by users. Defining how to prioritize the execution is the main task of job resource managers. We use machine learning techniques to improve this scheduling process by: (1) generating custom policies for specific architectures and workloads, and (2) selecting the best policy to apply to each machine given its current state.

Accelerating Biology and Neuroscience applications using GPUs

  • Many biological and neuroscience applications require very large amounts of computational power and can benefit from the massively parallel processing units available on GPUs. We developed algorithms in CUDA for simulations Hodgkin-Huxley networks, determination of gene regulatory networks and enumerating hitting set solutions. 

MACHINE LEARNING

Self-Supervised Learning of EEG Data using Neural Networks

  • EEG signals are generated from the collective activity of billions of neurons and allow us to monitor brain function using non-invasive electrodes. The captured signals are noisy and contain overlapped signals from many regions, making them difficult to interpret. We use self-supervised learning in neural networks to learn representations of EEG signals, allowing the detection of changes in neural processing when performing tasks in psychophysical experiments.    

Learning better HPC Scheduling Policies

  • Existing supercomputers must process many thousands of jobs (programs) submitted by users. Defining how to prioritize the execution is the main task of job resource managers. We use machine learning techniques to improve this scheduling process by: (1) generating custom policies for specific architectures and workloads, and (2) selecting the best policy to apply to each machine given its current state.

 

Analysis of Public Bus System Data 

  • Public bus systems are an essential part of urban mobility, due to their low cost and high capillarity. But providing acceptable for users is difficult due to limited system capacity, interactions with the city traffic, and disruptions caused by city events. We analyze GPS from public buses from São Paulo city to better understand the factors that impact the reliability of bus systems, to determine disruptions in the bus systems in real-time, and to predict the future states of the bus systems.

COMPUTATIONAL NEUROSCIENCE

Functional Connectivity in Neural Networks

  • The brain is composed of thousands of brain regions that must communicate in an organized way. Directed Functional Connectivity can be estimated using causality methods, such as Granger Causality and Partial Directed Coherence. We use computational models of mice connectomes to evaluate the applicability of these measurements in neural signals.

Self-Supervised Learning of EEG Data using Neural Networks

  • EEG signals are generated from the collective activity of billions of neurons and allow us to monitor brain function using non-invasive electrodes. The captured signals are noisy and contain overlapped signals from many regions, making them difficult to interpret. We use self-supervised learning in neural networks to learn representations of EEG signals, allowing the detection of changes in neural processing when performing tasks in psychophysical experiments.    

Universidade Federal do ABC (CMCC) - Campus SBC

Rua Arcturus, 03 - Jardim Antares, São Bernardo do Campo, SP, Brazil
CEP: 09606-070

©2018 by Raphael Yokoingawa de Camargo

This site was designed with the
.com
website builder. Create your website today.
Start Now