CV Lattes
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Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME) (Institutional affiliation from the last research proposal) Birthplace: Brazil
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)
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The convergence of Artificial Intelligence (AI) with cloud-based 5G networks creates unprecedented research opportunities to develop innovative applications in intelligent systems. However, integrating cutting-edge AI techniques into 5G networks brings several significant challenges, particularly in data collection and management, scalability of model training and deployment, interoperabi…
Deep learning techniques have shown impressive results in low-level cognitive task such as image and speech recognition as well as in high-level cognitive tasks such as question answering and stochastic planning.Yet, developing effective deep learning solutions is notoriously challenging as they require massive amounts of data and compute (often beyond limited budgets of typical users), …
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…
Probabilistic Sentential Decision Diagrams (PSDD) are discrete statistical models that allow the easy integration of probabilities and logic. In short, a PSDD is a logic circuit with a special syntax whose arcs (wires) are annotated with probabilities. The logic circuit e¿ciently specifies the distribution support as a logic constraint. This way, PSDDs can easily specify probability distr…
Neuro-symbolic approaches aim at achieving state-of-the-art performance with less data while providing more factually correct and consistent answers. In this research project, the student shall review the literature on neuro-symbolic approaches to question answering and other complex reasoning tasks, by implementing toolsets and evaluating on public benchmarks.
Probabilistic programming languages facilitate statistical model construction and inference. In this research project the student shall become familiar with the declarative probabilistic programming languages that compile to probabilistic circuits, as well as their parametric learning algorithms.
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 / 2 | Ongoing grants |
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| 3 / 3 | Ongoing scholarships in Brazil |
| 3 / 3 | Completed scholarships in Brazil |
| 10 / 9 | All research grants and scholarships |
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