About

🎯 My Interests

I have a deep interest in the probabilistic perspective to machine learning and artificial intelligence, especially in the identification of causal mechanisms via statistical quantities from data (aka Causal Inference). Other topics of interest include (but are not limited to):

• Learning Theory (bandits, game theory, statistical learning theory, etc.)
• Optimization (convex and non-convex optimization, matrix/tensor methods, sparsity, etc.)
• Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)
• Trustworthy Machine Learning (accountability, causality, fairness, privacy, robustness, etc.)

⚖️ My Core Scientific Values

• 🧪 Theory: data is not everything. The Data Mechanism is an important part of modeling.
• 🔓 Openness: science is best when people can build on each others' work. Sharing data, code, and ideas has benefitted everyone, and is also the responsibility of a scientist to pay forward.
• 🎯 Thoroughness: quality is dependent on taking great care throughout all steps.
• ♻️ Reproducibility: code is part of the scientific process. It should be documented and re-usable by anyone.

🥇 Bio

I received the Bachelor and M.Sc. degree in Computer Science from the Federal University of Paraíba (Brazil), in 2013 and 2015 respectively. I collected my Ph.D from the Department of Electronics, Information and Bioengineering at Politecnico di Milano (Italy) in 2019, where during my research I concentrated efforts on the design of smarter Physically Interactive Robot Games through the application of Artificial Intelligence and Probabilistic Machine Learning methods to support high quality Human-Robot interaction during play.