CV Lattes
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Universidade Federal de São Carlos (UFSCAR). Centro de Ciências Exatas e de Tecnologia (CCET) (Institutional affiliation from the last research proposal) Birthplace: Brazil
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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Machine Learning (ML) and Statistics have emerged as powerful disciplines in the fieldof data analysis, each offering unique perspectives and methodologies for extracting valuable insights from complex datasets. The goal of this work is to investigate how statistics can effectively evaluate theuncertainty of ML methods.The proposal consists of three interconnected aims that address differ…
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…
(Only some records are available in English at this moment)
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.…
Redshift is an astronomical measure that describes the shift of an electromagnetic wave toward the red end of the spectrum. This measure is crucial for quantifying the distance between the observer and astronomical objects (such as galaxies and quasars) and also for measuring how the universe is expanding. The most accurate method for redshift estimation is through spectroscopy. However, …
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…
(Only some records are available in English at this moment)
This project aims to develop machine learning methods to enhance the understanding and modeling of physical phenomena, with a particular focus on climate science and related inference problems. Simulation-Based Inference (SBI) provides a powerful framework for estimating nonlinear model parameters from observational data, which is crucial for tasks such as tuning parameterizations in atmo…
| 2 / 2 | Ongoing grants |
| 5 / 5 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 7 / 5 | Completed scholarships in Brazil |
| 1 / 1 | Ongoing scholarships abroad |
| 16 / 14 | All research grants and scholarships |
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