I am a data and technology professional from Nairobi, Kenya, currently pursuing an MSc in Data, Inequality and Society at the University of Edinburgh.

My interests sit at the intersection of:

• 🤖 Artificial Intelligence & Machine Learning
• 📊 Data Science & Analytics
• ⚖️ Responsible & Inclusive AI
• 🔎 Data Gaps, Data Silences & Representation
• 🌍 Data Inequality & Social Impact
• 🧭 Spatial & Geospatial Data Science
• 👥 Community Data Agency
• 🏛️ Data Governance & AI Ethics

The University of Edinburgh | 2026–2027

My current academic interests include:

• Artificial Intelligence
• Machine Learning
• Data Science
• Data quality and representation
• Algorithmic inequality
• Responsible AI
• Data governance
• Spatial and geospatial analysis
• Social and computational inequality
• Inclusive data systems

AI & Data
│
├── Artificial Intelligence
│   ├── Machine Learning
│   ├── Data-Centric AI
│   ├── AI for Science
│   └── Human-Centred AI
│
├── Responsible AI
│   ├── Fairness
│   ├── Bias
│   ├── Uncertainty
│   ├── Explainability
│   └── Accountability
│
├── Data & Inequality
│   ├── Data Silences
│   ├── Representation
│   ├── Missing Data
│   ├── Data Governance
│   └── Data Agency
│
└── Computational Methods
    ├── Data Science
    ├── Statistical Learning
    ├── Spatial Analysis
    ├── Geospatial AI
    └── Data Visualisation

I am interested in the spaces between what exists in the world and what becomes represented in data.

A central question guiding my work is:

Who is missing? What is missing? Where are the gaps? And what happens when computational systems learn from those gaps?

I want to explore reproducible approaches for:

1. Identifying gaps and underrepresentation in datasets
2. Measuring data quality and completeness
3. Understanding how missingness affects models and predictions
4. Quantifying uncertainty and limitations
5. Examining how inequality can propagate through computational systems
6. Designing more inclusive and accountable data and AI systems

My GitHub is evolving from a collection of technical projects into a research and experimentation portfolio.

You will find projects involving:

• 🤖 AI and machine learning
• 📊 Reproducible data analysis
• 🔎 Data quality and representation audits
• ⚖️ Responsible AI experiments
• 🧠 Model evaluation and uncertainty
• 🗺️ Spatial and geospatial analysis
• 📈 Interactive data applications
• 🔬 Research prototypes
• 🌍 Data projects focused on social and environmental challenges

Experiments exploring machine learning, model performance, uncertainty, representation and responsible AI.

Projects examining how datasets represent different populations, places and experiences — and what happens when important information is missing.

Projects using geographic data and spatial methods to investigate social, environmental and infrastructural questions.

Reproducible analyses, data pipelines, visualisations and decision-support tools.

Projects exploring how data and digital technologies can support more inclusive decision-making and strengthen community data agency.

Artificial Intelligence
├── Machine Learning
├── Data-Centric AI
├── AI for Science
├── Responsible AI
└── Human-Centred AI

Data Science
├── Statistical Learning
├── Data Analysis
├── Data Engineering
├── Data Visualisation
└── Reproducible Research

Responsible Technology
├── Fairness
├── Bias
├── Uncertainty
├── Explainability
├── Accountability
└── AI Governance

Data & Society
├── Inequality
├── Exclusion
├── Data Representation
├── Data Silences
├── Data Agency
└── Data Governance

Computational & Spatial Methods
├── Spatial Analysis
├── Geospatial Data
├── Spatial Machine Learning
├── Network Analysis
└── Computational Social Science

I am working towards a research career focused on AI, data science and responsible computational systems.

My aim is to develop the technical depth to build advanced AI systems while maintaining a critical understanding of the social contexts, assumptions and inequalities embedded within data.

Ultimately, I want my research to help answer a simple but important question:

How can we build intelligent systems that do not become more intelligent at reproducing the inequalities already present in society?