is an Associate Professor at the Department of Computer Science of the Institute of Mathematics and Statistics of University of São Paulo (Brazil). He has experience in the area of Artificial Intelligence, particularly with graph-based probabilistic models and its applications. He obtained his PhD from the University of Lugano (Switzerland) in 2013, a Master in Mechatronic Engineering and a Degree in Electrical Engineering from the University of São Paulo (Brazil). Between 2009 and 2013, he was a researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA) in Lugano, Switzerland. During 2014, he was a post-doctoral researcher at the Decision Making lab of University of São Paulo. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| TITULO |
| Articles published in other media outlets (0 total): |
| More itemsLess items |
| VEICULO: TITULO (DATA) |
| VEICULO: TITULO (DATA) |
Bayesian networks allow for the compactrepresentation of uncertain knowledge. There is strong evidence from computational complexity that performing inference in them takes timeexponential in the network treewidth, which measures the complexity of the model.Bounding the treewidth of the networks decreases their representational power. We can counter that decrease without increasing the co…
This text describes an undergraduate research project for a study on probabilistic circuits, which are generative probabilistic models in which it is possible to realize different types of inference in an exact and computationally efficient way. A probabilistic circuit is specified through its structure, a directed acyclic graph that models the conditional relations of (in)dependence betw…
This is a proposal for a Scientific Initiation project that aims to study Probabilistic Circuits, which are parametric probabilistic models based on neural networks that allow several inferences to be computed accurately and efficiently. Throughout the project, the student will become familiar with the main algorithms and techniques for parametric learning of these models from data, as we…
Influence diagrams are graph-based probabilistic models for representing decision-making problems under uncertainty. These diagrams allow for the clear communication of complex decision making situations, being one of the main tools in the probabilistic analysis of structured decision problems. Solving an influence diagram refers to finding an optimal set of rules that map observations i…
| 2 / 1 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 2 / 2 | Completed scholarships in Brazil |
| 5 / 4 | All research grants and scholarships |
| Associated processes | |
CV Lattes
ORCID