Rodrigo Barros

Computer Science

Computer scientist Rodrigo Coelho Barros completed his undergraduate studies at the Federal University of Pelotas, his hometown. He earned his master’s degree from the Pontifical Catholic University of Rio Grande do Sul in the same field and earned his doctoral degree from the Institute of Mathematical and Computer Sciences at the University of São Paulo, specializing in computer science and computational mathematics. Barros’s work has been recognized by the Brazilian Computer Society and CAPES, receiving awards for the country’s best computer science thesis. His research delves deeply into artificial intelligence models.

Currently, he is the coordinator of the Machine Intelligence and Robotics Research Center at PUCRS. His scientific research has fostered a love for travel, and he takes pride in having explored the glaciers of Alaska and the volcanoes of Hawaii. He aspires to visit the Sahara desert and the Egyptian pyramids. In addition to his professional pursuits, Barros is an amateur film critic and an avid fantasy literature reader.


AI for Social Good: Developing Fair, Explainable Neural Networks Resilient to Confounding Factors and Requiring Minimal Supervision
Science / Computer Science

Neural networks largely drive the recent Artificial Intelligence revolution and are computational mechanisms inspired by the workings of the human brain. These networks, composed of hierarchically organized artificial neurons, can learn from human-annotated examples. In our research group, we are striving to address some of the primary limitations of these methods. Our goal is to understand how to construct neural networks that make fair decisions, even when the data gathered from the real world might reflect societal injustices, such as racial or gender disparities. Additionally, we are exploring ways to elucidate the decisions made by neural networks, which are often considered inscrutable “black boxes.” Lastly, we aim to determine how networks can learn in scenarios where there is a scarcity of human-annotated data, a situation that applies to the vast majority of today’s available data.

Amount invested

R$ 100,000.00

Open Calls

Science Call 3
  • Topics
  • artificial inteligence
  • data
  • Neural networks