Teaching computer programming (MATLAB) to first-year engineering students, mentoring undergraduate thesis projects in disaster resilience, and supervising internal projects in disaster management, including data science, data collection, natural language processing, and data visualization following FAIR data principles.
Development of a ML framework to estimate flood economic losses by integrating resilience indicators and XAI techniques. The approach identifies drivers of impact, evaluates the effectiveness of measures, and supports informed decision-making.
Research for possible solutions to enhance semantic interoperability and data provenance in scientific repositories, taking the genomic area as a use case. This investigation bases on FAIR data principles, nanopublication and FAIR Knowlet schema.
Tragically, forest fires worldwide are a trending topic nowadays. Data analysis can be essential for understanding, sharing, and generating information about these fires. In this article, we perform a data exploration to understand better the fires that occur annually in the Pantanal region. 7 min read.
Starting from the Projeto Queimadas data, we will develop a simple app with Streamlit to visualise possible fires worldwide captured by satellites based on the Pydeck. 5 min read.