Currently engaged in master's studies and research on physics-informed neural networks (PINN) for petroleum reservoirs, integrating Partial Derivative Equation (PDE) models into deep learning applications.

Academic qualifications include a bachelor's degree in physics and a master's degree in artificial intelligence at PUC Rio within the electrical engineering department. Competencies encompass various Data Science frameworks, AWS Cloud Practitioner certification, extensive experience in Python (4.5 years), SQL, and expertise in machine learning and deep learning.

Career credentials demonstrate a diverse range of roles. Engagements include participation in a focused AI and data program at a Brazilian research center, contributions to digital transformation initiatives at an insurance company through analytics, data science, and AI, and involvement with a GovTech innovation laboratory. Additionally, experience as a Junior Researcher encompassed data analysis, scraping, and visualization tasks at the Federal University of Rio de Janeiro.

Programming experience totals four years with a focus on Python within data science and analytics, and two years in object-oriented structures. Comprehensive learning through distance platforms amounts to approximately 300 hours dedicated to machine learning and deep learning courses. Near completion is a 360-hour specialization in Machine Learning and Artificial Intelligence along with a scientific project at PUC Minas.

Proficiency in developing data science and artificial intelligence solutions is supported by the utilization of frameworks including Pandas, Numpy, Scipy, Sklearn, TensorFlow, Git, Dask, PowerBi, Matplotlib, Plotly, Seaborn, AWS, Azure, NLTK, StreamLit, Selenium, SQL, NoSQL, among others.