Achievements & Recognition
Selected awards, academic milestones, and continuous professional development
Highlights
1st Prize
Wits Mining Digital Hackathon · 2022
Member of the winning team for the University of the Witwatersrand’s 24-hour Digitalisation in Mining challenge.
MSc Statistics, Cum Laude
University of KwaZulu-Natal
Completed an MSc in Statistics with a dissertation on the spatial and joint modelling of chronic illness and household wealth among South African adults.
Published Researcher
2 peer-reviewed publications
Published work spanning Bayesian deep learning, uncertainty estimation, and computer security.
Wits Mining Digital Hackathon
1st Prize — Digitalisation in Mining
In 2022, I was part of the winning team at the University of the Witwatersrand Hackathon, where the challenge focused on digitising and extracting value from mining information.
Our solution addressed the problem from several angles:
- digitising mining documents using Optical Character Recognition (OCR)
- applying text analytics and topic modelling to extract insight from mining documents
- analysing social-media data to identify issues and regions of interest
- identifying geographic areas with relatively high or low mining potential
- modelling factors associated with mining potential and safety
- predicting mining potential for selected regions
- simulating scenarios involving natural resources, environmental sensitivity, and water availability
The project combined data science, statistical modelling, NLP, OCR, geographic reasoning, and application development under a tight hackathon deadline.
The winning team received a R15,000 cash prize and NEMISA training courses valued at R50,000.
Academic Milestone
MSc in Statistics — Cum Laude
I completed my MSc in Statistics at the University of KwaZulu-Natal.
Dissertation: Spatial and Joint Modelling of Chronic Illness and Household Wealth among South African Adults
Final mark: 75%
The research used advanced statistical methods including:
- Generalized Additive Models
- spatial modelling
- Markov Random Fields
- joint modelling
- copula-based frameworks
This work strengthened my interests in spatial statistics, health and wealth inequality, statistical inference, nonlinear modelling, and interpretable quantitative research.
Microsoft Learn Achievements
I use structured technical learning to strengthen areas that complement my practical data-science work. The items below are Microsoft Learn training achievements, rather than professional certification exams.
Machine Learning & AI
MLOps, DevOps & Delivery
Data Analytics
Continuous Technical Development
My learning has expanded beyond individual courses into hands-on implementation. I am currently building a structured repository around modern AI engineering, including:
- Generative AI and LLM fundamentals
- API-based and local-model workflows
- Retrieval-Augmented Generation (RAG)
- knowledge-grounded responses
- tool calling
- agentic AI and multi-step workflows