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: Argentina
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). He is currently full professor at Universidade de São Paulo. Has experience in Probability and Statistics, focusing on Inference in Stochastic Processes (Source: Lattes Curriculum)
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In this project, we propose to study three problems. The first two are related to the identification of hidden variables, such as communities or block partitions of a set of nodes, from the observation of an additional structure. For the stochastic block model, we will rigorously study a very natural method based on the harmonic functions of a graph with connection probabilities determine…
The Thematic Project proposed here brings together a significant portion of theProbability and Stochastic Processes research group in the state of São Paulo. This group iswell-established in the Departments of Statistics and Mathematics at USP, Unicamp, UFSCar,and UFABC and has gained international visibility and recognition due to several factors: (i) itsconsistent and noteworthy publica…
A mathematical center is proposed to integrate mathematical modeling with basic and applied lines of research at the frontier of neuroscience. The proposal is a response to the increasing importance of mathematical structures in theoretical neuroscience. One of the underlying reasons is the huge mass of data generated by recent experimental capabilities of research, whose analysis require…
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…
(Only some records are available in English at this moment)
This project will study a probabilistic model related to the classic Ising model exhibiting community structure. We will develop different estimation criteria and techniques for detecting the communities based on a partial sample of the vertices of the interaction graph. (AU)
This proposal's main objective is to study probabilistic models with a dependency structure expressed through graphs and their statistical properties in data with temporal evolution. The main model that will be studied is the Markov random field model on graphs. A possible contribution of this project is the extension of these models for the analysis of continuous data with temporal evolu…
The main goal of this project is to study the problem of order estimation in the class of Hidden Markov models. This is a classic problem in the inference literature for stochastic processes, with some advances towards order estimation. However, the consistency of the Bayesian Information Criterion (BIC) still remains an open question. This study, which will be conducted based on recent r…
(Only some records are available in English at this moment)
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…
| 3 / 3 | Ongoing grants |
| 8 / 7 | Completed research grants |
| 4 / 4 | Ongoing scholarships in Brazil |
| 5 / 3 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 22 / 19 | All research grants and scholarships |
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