received a Bachelor´s degree in Statistics from Universidade Estadual de Campinas (1985), MsSci in Mathematics from Associação Instituto Nacional de Matemática Pura e Aplicada (1988) and Phd. in Statistics from University of Wisconsin - Madison (1993). She has expertise in Probability and Statistics, focusing on Special Stochastic Processes, acting on the following subjects: invariant measure, perfect simulation, percolation, spatial point processes and loss networks. (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…
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…
Markov Chains with variable length have been widely explored in the literature of probability andstatistics. However, little is known when the transition probabilities depend on exogenous variables. Wepropose novel context algorithms and search for the conditions and asymptotic behavior of the estimatedcontext trees. (AU)
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Markov random fields are often used when analyzing image data. In some applications, these analyses become difficult due to the presence of textures in images, which hinders the use of spatial models with simple interaction neighborhoods because they induce very sophisticated correlation structures. The main goals of the project are (i) to study and develop probabilistic methods for ident…
The objective of this project is to study a spatial network model with set of vertices given by a locally finite set of R^d. Our goal is the construction of an infinite volume Gibbs measure for which the probability of observing a graph is based on the graph's statistics (triangles, stars, vertices degree) and it penalizes edges between nodes that are very far apart.
Poisson processes are one of the most popular point process models for count data. Many natural phenomena can be described with this process, and estimation of parameters is usually easy. The basic assumption for a spatial Poisson process is independence between disjoint sets. However, data from many applications show interaction between points, either by clustering or repulsion. In this …
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In this research project we aim to study new statistical inference approaches for networks' analysis. In particular we focus on networks that exhibit community structure, that is, the nodes are organized into groups with similar connection patterns. We are interested in developing new statistical approaches applied to the study of a single network as well as for random samples of networks.
n many areas of science one aims to study latent sub-population based only on observations of aggregated population observation. In this work, aggregated curves will mean linear combination of functional data that cannot be observed individually. We assume that several aggregated curves with linearly independent coefficients are available and each aggregated curve is an independent partia…
| 2 / 2 | Ongoing research grants |
| 11 / 9 | Completed research grants |
| 11 / 7 | Completed scholarships in Brazil |
| 3 / 2 | Completed scholarships abroad |
| 27 / 20 | All research grants and scholarships |
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CV Lattes
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