Researcher in Probability, Statistical and Artificial Intelligence
Researcher Profile
I study probability theory, statistical inference and statistical learning theory, with emphasis in calculations theoretical, as a foundation for understanding modern machine learning systems.
My work is centered on the mathematical and statistical structure of artificial intelligence models. I am particularly interested in how learning systems can be derived, analyzed, and interpreted through rigorous deterministic or non-deterministic reasoning — including their assumptions, limitations, and expressive capabilities.
While my primary orientation is theoretical, I also develop and implement models in practice, connecting foundational principles with computational methods in machine learning and data analysis.
Research Areas
- Probability Theory
- Statistical Inference (Frequentist and Bayesian)
- Statistical Learning
- Artificial Neural Networks
- Natural Language Processing
- Generative Modeling (Generative AI)
- Large Language Models (LLMs)
- Multi-agent Systems
Research Questions
How can artificial neural networks be formally derived using probabilistic and statistical principles?
How can models based on deep neural networks be interpreted?
How can generative models be understood and interpreted in terms of latent variable structures and inference?
Can the estimated parameters of a LLM be interpreted for tasks applied in reality?
Is current probability theory sufficient to provide an axiomatic basis for generative models?
Academic background
Languages & tools i work with
