Computer Engineering | Geospatial Data Science | Open Science

Associate Professor at UFMA | MSc/PhD in Applied Computing @ INPE

I design project-based courses that blend theoretical foundations, hands-on coding practice, and real-world environmental applications. All materials are open-access, versioned on GitHub, and bilingual (PT/EN) where possible — aligned with FAIR principles and reproducible science practices.

📚 Core Courses (English Summaries)

⚙️ Compilers

• Level: Undergraduate (Computer Engineering)
• Languages/Tools: Java, Haskell, Python, nand2tetris framework
• Focus: Lexical analysis, parsing, code generation, and optimization through hands-on compiler construction
• Open Resources: GitHub Repo • Sample Project: Build a Simple Interpreter
• Why it matters internationally: Develops transferable skills in formal languages, automata theory, and tooling — foundational for PL research, verification, and compiler engineering roles.

🧮 Algorithms & Data Structures

• Level: Undergraduate (Computer Engineering)
• Languages: Python, Java, C
• Focus: Algorithm design patterns, complexity analysis, and efficient data organization for geospatial and environmental datasets
• Project Example: Implement spatial indexing structures (R-trees, quadtrees) for land-use data queries
• Global relevance: Core competency for technical interviews and research in computational geography, optimization, and large-scale data processing.

🔄 Programming Paradigms

• Level: Undergraduate (Computer Engineering)