teaching

🛰️ Google Earth Engine (GEE) Resources

I have developed specialized materials for using cloud computing in Remote Sensing. Note: These resources are currently available in Portuguese (PT-BR).

• Introduction to Satellite Image Processing and Analysis with GEE A comprehensive guide and talk covering the fundamentals of the GEE JavaScript API, focusing on image collections, reducers, and environmental analysis workflows.

• GEE in Remote Sensing and Geoprocessing Education Educational material designed to integrate cloud-based workflows into undergraduate introductory courses, replacing traditional local processing with planetary-scale data analysis.

💡 For a complete list of my teaching materials, including labs, workshops, and tutorials, please visit my Notion.

Summary

The sections below provide English summaries of the core courses I regularly teach at UFMA, highlighting their technical focus and international relevance.

⚙️ 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
• 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)
• Paradigms Covered: Imperative, Object-Oriented, Functional (Haskell), Logic
• Focus: Comparative analysis of paradigms; when and why to choose each approach for scientific computing problems
• International value: Prepares students for diverse codebases and research environments that mix paradigms (e.g., data engineering pipelines).

λ Functional Programming

• Level: Undergraduate elective / Graduate module
• Language: Haskell
• Focus: Pure functions, type systems, monads, and declarative problem-solving for reproducible scientific workflows
• Connection to research: Functional approaches support verifiable, side-effect-free geospatial data transformations

🗺️ Introduction to Geoprocessing (Graduate)

• Program: Professional Master’s in Environmental Science & Technology (PPGCTAmb)
• Tools: QGIS/PyQGIS, PostGIS, TerraLib, OGC standards
• Focus: Spatial data models, coordinate systems, geoprocessing workflows, and environmental modeling fundamentals
• INPE Connection: Builds on methodologies developed during my PhD research in dynamic land-use/land-cover modeling at INPE
• Output: Students produce reproducible geoprocessing pipelines for regional environmental analysis

💻 Introduction to Computers (Graduate)

• Program: Professional Master’s (PPGCTAmb / PROFCOMP)
• Focus: Information representation, computer architecture fundamentals, operating system concepts for scientific workflows