graduation at Bacharelado em Estatística from Universidade de São Paulo (2008), master's at Estatística from Carnegie Mellon University (2011), master's at Probability and Statistics from Universidade de São Paulo (2010) and doctorate at Estatística from Carnegie Mellon University (2014). Has experience in Probability and Statistics, focusing on Nonparametric Inference (Source: Lattes Curriculum)
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In the last decade, computational advancements have made neural networks reemerged as a powerful tool for performing supervised learning tasks such as classification and regression. Nonetheless, this tool has been subutilized as a way of performing statistical inference. For instance, solutions given by neural networks are typically black-box and therefore hard to interpret. In this work…
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Most supervised machine learning methods yielda point prediction for a target, $Y \in \sY$,based on features, $\X \in \sX$. Recently, a large body of work in statistics and machine learning has been devoted to develop methods that are able to quantify the uncertainty over point predictions. These go from prediction regions to conditional density estimators and quantile regression methods.…
Reproducing Kernel Hilbert Spaces (RKHS) methods are a broad family of statistical learning models that builds upon the notion of a kernel, which can be interpreted as a similarity measure between data observations. For instance, ridge regression, support vector machines - including their kernelized versions - and smoothing splines can be cast to a generic function optimization problem …
Clustering methods have the goal of grouping together sample points that have similar properties. Several clustering techniques have been developed. An important question in practice is how to choose which method to use. While several approaches to answer this question have been proposed, they do not focus on hierarchical clustering methods because these do not yield a single partition o…
Hypothesis tests are largely used by the scientific community. In particular, goodness of fit tests are extremely important, because they allow us to test if scientific theories adequately describe the framework of a dataset. Recently, several tests for high-dimensional data have been proposed, which enables these statistical methodology to be applied in complex objects such as images, tr…
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| 5 / 5 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 6 / 4 | Completed scholarships in Brazil |
| 12 / 10 | All research grants and scholarships |
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