has graduation at Bacharelado at Ciência da Computação by Universidade Federal de Pelotas (2007), master's at Ciência da Computação by Pontifícia Universidade Católica do Rio Grande do Sul (2009), and Ph.D. at Ciências da Computação e Matemática Computacional by Universidade de São Paulo (2013). Currently is Associate Professor at Institute of Science and Techonology at Federal University of Sao Paulo (UNIFESP). Has coordinated national and international research projects funded by CNPq and FAPESP. Has experience in the area of Computer Science, with emphasis on Machine Learning, Data Mining and Bio-inspired Computation. Since 2014, is granted by CNPq with a Research Productivity Award. (Source: Lattes Curriculum)
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The Applied Research Center for in Artificial Intelligence, IARA, is formed by a multidisciplinary team, with national experts of internationally recognized for their competence in artificial intelligence, internet of things telecommunications and smart cities. The center, hosted in São Carlos, in the state of São Paulo, will work in network with researchers from all Brazilian regions, or…
Neural Architecture Search (NAS) is a research field that utilises evolutionary algorithms to generate/optimise neural networks. In recent years, NAS-based algorithms have created models that outperform hand-craft designs in some tasks, settings the new state-of-the-art. In this research project, we will utilise grammar-based multi-objective NAS algorithms to generate deep convolutional n…
The use of meta-learning for algorithm recommendation is a research field that has been extensively explored in recent years. Meta-learning systems for recommending algorithms can be divided into two groups: (a) systems that perform selection of algorithms or models based on meta-features; and (b) systems that search for the best possible classification algorithm in a given space of algor…
Hierarchical Multi-label Classification (HMC) is a complex problem, in which classes involved are structured in a hierarchy with hundreds or even thousands of classes. Additionally, instances can be simultaneously classified into more than one path in this hierarchy. These problems are very common, for example, in protein function prediction and annotation of medical images. Among the dif…
Decision-tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. In previous work, we proposed the…
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Linear Genetic Programming is a type of algorithm able to evolve code in a programming language. LGP has been applied in a wide range of problems, as machine learning, navigation, and routing. Its stochastic search process has no knowledge to generate good solutions nor is able of avoiding poor ones, which decreases its efficiency. In this project, two topics will be explored in order to …
In the context of machine learning, representing a dataset by graphs have been studied in the literature, especially in the semi supervised learning area. The principal feature of the techniques based on graphs (networks) is in the way data is represented in which network vertices represent the data and the edges represent the distances/similarities (relations) between the examples. Among…
The identification of macro-parameters (used as building-blocks) is one of the most important tasks for automatically evolving decision-tree induction algorithms. These building-blocks will be used by the GP for automatically evolving new decision-tree induction algorithms. In this context, this work proposes to investigate other building-blocks as well as some variations of the ones prop…
Linear Genetic Programming (LGP) is a kind of algorithm capable of evolving computer code which has been successfully applied in various problems, such as Machine Learning (ML), navigation, and routing. As in other Evolutionary Algorithms (EAs), its stochastic search process neither has the knowledge to produce good quality solutions nor is able to avoid poor quality solutions, which redu…
Decision-tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. In previous work, we proposed the…
| 3 / 3 | Ongoing research grants |
| 6 / 6 | Completed research grants |
| 5 / 3 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 16 / 14 | All research grants and scholarships |
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