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
I am a PhD candidate in Statistics at the Interinstitutional Graduate Program in Statistics (PIPGES) at UFSCar-USP. I hold a bachelor's degree in Statistics from the Federal University of São Carlos (2022). My research focuses on Statistical Learning and Uncertainty Quantification in Machine Learning, with experience in Probability and Statistics. I also have advanced programming and development skills in R and Python. (Source: Lattes Curriculum)
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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.…
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…
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…
| 1 / 1 | Ongoing scholarships in Brazil |
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