graduation at Bacharelado em Matemática from Fac. de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata (2002) and doctorate at Computer Science from Universidade de São Paulo (2007). Has experience in Probability and Statistics, focusing on Inference in Stochastic Processes (Source: Lattes Curriculum)
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The Probability and Stochastic Processes group, consolidated and working at the Statistics and Mathematics departments of USP, UFSCar, Unicamp and UFABC, has international visibility and recognition due to \textit {(i)} its frequent and relevant publications in high standard international journals, \textit {(ii)} sucessful organization of conferences that are on the agenda of the best res…
The main goal of this research project is to study model selection methods for high-dimensional data analysis. The data will be composed by different types and structures, such as graphs and random networks, large vectors or temporal space data. The focus of this project is related to predictive models and the objective is to select models with adequate size in order to minimize the predi…
The main goal of this research project is to study methods of statistical inference to identify different structural aspects in stochastic processes in high dimensions. We will consider principally the estimation of the graph of interaction in Markov random fields, also known in the statistical literature as graphical models, and the identification of change points for stochastic process…
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Neural network models have become one of the most promising statistical models for complex data analysis. The usual neural networks can be used in classification or regression problems, depending on the nature of the data. The usual models of neural networks assume that the analyzed data are independent. In turn, recurrent neural network models allow the analysis of data with dependency, …
Over the past years, with the computational advances, statistics have undergone drastic changes, especially with regard to the increasing capacity of data storage. For each individual, more and more features have been collected, then the number of parameters to be estimated have becomes much larger than the number of observations. It is the case, for example, of areas as biology or applic…
The main goal of this research project is the construction and study of perfect simulation algorithms for probabilistic networks. We will consider the problem of perfect simulation of Markov random fields taking values on a finite alphabet, both in the case of a fixed underlying graph as well as in the case of a random underlying graph. For the latter we will consider principally the expo…
The main goal of this research project is the development of perfect simulation algorithms for Markov random fields defined on graphs, taking values on a finite alphabet. In order to achieve these goals, we will adapt some techniques from the literature and we will study the theoretical properties of the algorithms proposed. In particular we will focus our attention on the Coupling form t…
In this project we will be concerned with the statistical inference of graphical models exhibiting an evolution in time. In particular, we will focus on the following research lines:- The development of new probabilistic models for the description of brain activity, taking into account variable range dependence in time and space. - The use of penalized maximum likelihood and convex progra…
| 1 / 1 | Ongoing research grants |
| 5 / 4 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 4 / 2 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 13 / 10 | All research grants and scholarships |
| Associated processes | |
CV Lattes
ORCID