Caio Lima
I am a Statistics student and researcher focused on the mathematical foundations of Machine Learning, Statistical Inference, and Generative Modeling.
My work is driven by a fundamental question: how can intelligent systems be understood, constructed, and interpreted through rigorous mathematical structures?
I am particularly interested in probability theory, stochastic processes, and the theoretical mechanisms underlying neural networks and large language models. Rather than treating models as black boxes, I aim to derive and analyze them from first principles — understanding their behavior, limitations, and expressive power.
Research Vision
My long-term goal is to contribute to the development of interpretable and theoretically grounded artificial intelligence, bridging the gap between statistical theory and modern machine learning systems.
I am currently exploring:
- Probabilistic modeling and statistical inference
- Generative models and latent variable methods
- Neural networks from a mathematical perspective
- Natural Language Processing (NLP) and Large Language Models (LLMs)
- Scientific computing and document systems (LaTeX, structured knowledge)
Background
- Bachelor’s Degree in Statistics — Federal University of Pará (in progress)
- Undergraduate researcher (PIBIC) in Artificial Intelligence and Statistical Modeling
Personal Note
Beyond formal research, I am deeply interested in developing a visual and intuitive understanding of mathematics — combining abstraction, structure, and interpretation.
I see mathematics not only as a tool, but as a language capable of expressing complex systems, intelligence, and patterns in their purest form.