I have a bachelor's degree in Computer Technology and additional specialization in Applied Machine Learning. My work focuses on geospatial risk analysis, telemetry data, predictive modelling, APIs, and decision-support systems.
A curated selection of projects that show end-to-end technical work: data ingestion, modelling, evaluation, APIs, and domain-specific reasoning.
Built a geospatial machine learning pipeline for flood and landslide-related risk analysis using public GIS data, spatial preprocessing, feature extraction, modelling, and evaluation.
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Designed a structured telemetry data system using FastF1 data, PostgreSQL-ready storage, session processing, lap-level features, and API-oriented project architecture.
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Developed analysis modules for tyre degradation, driver behaviour, lap-time trends, and strategy comparison across race sessions using weather and telemetry-derived features.
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I have a bachelor’s degree in Computer Technology and additional specialization in Applied Machine Learning, with a strong interest in applying AI and data systems to real operational problems. My work combines machine learning, software development, APIs, data engineering, geospatial analysis, and domain-specific modelling.
I care about building systems that are understandable, reproducible, and useful beyond a notebook: clear project structure, documented assumptions, evaluation, version control, and practical deployment paths.
Moving from analysis notebooks into reusable modules, APIs, and documented pipelines.
Using predictions, simulations, and uncertainty to support better technical decisions.
Working with messy, domain-heavy datasets such as GIS, telemetry, weather, and time-series data.
Tools and areas I use across machine learning, data engineering, and software projects.
A curated overview of the technical areas developed through my B.Sc. in Computer Technology and additional specialization in Applied Machine Learning.
Supervised learning, deep learning, data mining, preprocessing, computational intelligence, model evaluation, and applied ML workflows.
Object-oriented programming, modular design, debugging, testing, APIs, version control, and system development across multiple languages.
Relational database design, SQL, information systems, structured storage, data processing, and pipeline-oriented thinking.
Algorithms, data structures, complexity, abstraction, recursion, functional programming, and problem-solving fundamentals.
Software security, applied cryptography, lower-level programming, secure system thinking, and understanding how systems behave closer to the hardware.
Web fundamentals, HTML, CSS, JavaScript, UI thinking, dashboards, and translating technical outputs into interfaces people can use.
A clickable overview of the technical areas developed through my Computer Technology degree, Applied Machine Learning studies, and independent project work.
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Practical experience with supervised learning, deep learning, data mining, preprocessing, feature engineering, model evaluation, and applied ML workflows.
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Experience with object-oriented programming, modular design, debugging, testing, version control, APIs, and building maintainable software systems.
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Experience with relational database design, SQL, information systems, structured storage, data processing, and pipeline-oriented data modelling.
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Academic foundation in software security, cybersecurity, operating systems, computer networks, concurrent programming, and lower-level system behaviour.
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Strong academic foundation in algorithms, data structures, complexity, discrete mathematics, functional programming, language semantics, parsers, interpreters, and type systems.
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Experience translating technical outputs into usable interfaces through dashboards, APIs, web fundamentals, and user-facing project presentations.