Master's student in Metrology at the National Institute of Metrology, Quality and Technology — Inmetro
Data Science, Machine Learning, and Deep Learning enthusiast
Currently researching uncertainty estimation in vehicle fuel consumption and emissions models
Interested in Python, data analysis, neural networks, Laplace approximation, and probabilistic models
I hold a degree in Production Management from IFRJ and I am currently pursuing a Master's degree in Metrology at Inmetro, where I work with Machine Learning applied to uncertainty estimation in deep neural networks.
My research explores techniques such as Learnable Uncertainty under Laplace Approximations — LULA, Laplace Approximation, Deep Ensembles, model calibration, and robustness analysis in scenarios involving out-of-distribution data.
Besides academic research, I am also interested in data analysis, data visualization, process automation, and practical projects using Python.
- Machine Learning
- Deep Learning
- Uncertainty Quantification
- Bayesian Neural Networks
- Laplace Approximation
- Data Analysis
- Data Visualization
- Python for Data Science
- Vehicle fuel consumption and emissions modeling
