I solve problems using math. I am a Computational Mathematics graduate from Universidade Federal de Minas Gerais. Since very early in my undergraduate program, I got involved in undergraduate research at the Laboratory of Information Security, Cryptography, Privacy, and Transparency (INSCRYPT), studying formal methods of Quantitative Information Flow, a subarea of Information Theory with applications in security, privacy, algorithmic fairness, machine learning, and several others.This research led me to work as an intern at the same lab, where the team worked along with the governmental agency Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP) to model the privacy in their statistical disclosures of the educational census, which have a direct impact on more than 25% of the Brazilian population. In INSCRYPT, I learned more about data visualization, mathematical modeling, and privacy attacks.Soon after, I was a Machine Learning Engineer intern at MOST Specialist Technologies, where I projected, developed, deployed, and monitored machine learning models. Acquired experience in developing MLOps pipelines using AWS Sagemaker, creating APIs in Flask and FastAPI to deploy the models for inference, building CI/CD pipelines in GitLab, and several AWS products.Throughout my years at university, I also got involved in student representation, where I listened to their problems, discussed with professors to ensure the student's learning experience, and was their voice to shape the course's rules. I also helped organize events where the students practiced their communication and presentation skills. I was a member of the program Programa de Educação Tutorial (PET), where I assisted in the reformulation of the pre-calculus course's curriculum. This experience enhanced my ability to communicate complex ideas clearly.I'm interested in utilizing mathematical models to address real-world problems, leveraging technology to enhance people's lives, engaging in scientific communication, and creating podcasts.