About Me
I'm an impact-driven Computer Scientist specializing in data science, AI, and software development. I combine technical depth with leadership and communication skills developed through research, mentorship, and participation in global technology programs.
Current Focus
Research Fellow
Deep Learning IndabaX Uganda AI Lab (2025)
Software Developer
ChecMed UK LTD (2024–present)
Research Interests:
LLMs, Generative AI, Voice AI, and Applied Machine Learning for Social Impact
Education
Bachelor of Computer Science
Kabale University (2021–2024)
Experience
Software Developer
Mikya Pty Ltd
Handle end-to-end product development from UI/UX design and frontend implementation to backend integration for both web and mobile applications. Document processes clearly, collaborate with clients during meetings, and ensure smooth deployment and maintenance across all systems.
Software Developer
Checmed UK LTD
Build mobile and web apps integrated with FastAPI and Firebase. Implement CI/CD, optimize performance, manage authentication, and maintain clear documentation.
Research Fellow
Deep Learning IndabaX Uganda AI Lab
Contribute to lab projects through coding, experiment documentation, and team presentations.
Trainee
Huawei ICT Competition Training
Hands-on work in AI, Cloud, and Big Data; enhanced Python, Git, and cloud deployment skills.
Software Engineer Intern
Uganda Telecom Limited
Field troubleshooting, base-station repairs, and service restoration documentation.
Skills & Expertise
Technical Skills
Programming Languages
Machine Learning & Data Science
Web & Mobile Development
Databases & Backend
Soft Skills
Problem-solving
Analytical approach to complex challenges
Critical Thinking
Data-driven decision making
Leadership
Team collaboration and mentorship
Communication
Clear technical and non-technical presentation
Certifications & Awards
Huawei Cloud Certified Developer Associate (HCCDA-Tech Essentials)
Huawei Certified Cloud Developer Associate (HCCDA - AI)
Huawei Certified ICT Associate - Cloud Service
Huawei Cloud Service Computing Micro Certification
Huawei ICT Competition 2024 – 2025 (Global Final): Second Prize (Cloud Track)
Huawei ICT Competition 2023 – 2024 (Global Final): First Prize (Network Track)
Conferences & Programs
Deep Learning Indaba Rwanda 2025
Deep Learning Indaba Senegal 2024
Deep Learning Indaba Ghana 2023
IndabaX Uganda (2022 – 2025)
IndabaX Rwanda 2023
YouthMappers Summit 2023
Huawei ICT Competition (2023 – 2025)
Featured Projects
Here are some of my latest projects that I've worked on.
Exploring Large Language Models (LLMs) and Agent-Based Modeling for Policy-Making in Uganda
This ongoing project investigates how large language models can complement agent-based simulations to enhance policy formulation and evaluation in Uganda. The team is building hybrid models that allow policymakers to simulate societal responses, predict outcomes, and interpret public data using natural language interfaces. By combining LLMs with data-driven ABM frameworks, the project aims to promote evidence-based decision-making, transparency, and AI-assisted governance tailored to local contexts.
Afriverse AI — Preserving African Culture through Generative AI
Afriverse AI is an ongoing collaborative project exploring how generative AI can preserve, represent, and celebrate African culture through technology. The team is designing creative pipelines using diffusion models and large language models to recreate traditional attire, proverbs, and stories, showcasing how AI can capture cultural depth while maintaining authenticity and inclusivity. The project emphasizes digital preservation, creative accessibility, and the use of AI as a bridge between heritage and innovation across Africa.
Predictive Models for HIV Drug Fosamprenavir (FPV) Resistance Detection Using Genetic Sequences
The team developed machine learning models leveraging HIV-1 protease genetic sequences to predict resistance to the drug Fosamprenavir (FPV). Using curated data from the Stanford HIV Drug Resistance Database, the project applied CNN, Logistic Regression, XGBoost, and LSTM models to classify resistant and non-resistant strains with up to 85% accuracy.
Network Traffic Anomaly Detection System
Developed an intelligent intrusion detection system using the UNSW-NB15 dataset to identify anomalies in network traffic that may indicate cyberattacks. The project applied Random Forest, Gradient Boosting, CNNs, and LSTMs to classify normal and malicious traffic based on extracted flow features. Designed the data preprocessing and model evaluation pipeline to improve accuracy and minimise false positives compared to traditional rule-based detection methods. Presented this at the Deep Learning Indaba Ghana 2023, demonstrating how deep learning can strengthen cybersecurity and proactive threat monitoring in modern networks.
Heart Disease Prediction Using Machine Learning
Designed a machine learning model to predict the likelihood and severity of heart disease using the UCI Heart Disease dataset. The system combined Logistic Regression, Random Forest, XGBoost, and Neural Networks to analyse clinical attributes such as blood pressure, cholesterol, and heart rate, helping identify high-risk individuals. Through feature selection, model tuning, and evaluation with metrics like ROC-AUC and F1-score, the project achieved strong predictive performance. This work demonstrated the role of AI in preventive healthcare and the potential of data-driven insights to support early diagnosis and medical decision-making.
Get In Touch
Let's collaborate on innovative solutions that make a difference.
GitHub
github.com/ginareigns