→Full-stack & forward-deployed engineer. I build software end-to-end and embed with the people who use it — shipping in places where most tools don't reach.
Recent UC Berkeley MIMS graduate. Ex Co-founder & CTO of Afya — USSD-based EMR for Sub-Saharan Africa. Forward-deployed engineer who's architected, shipped, and handed off production systems on-site.
Click a skill → see projects → click project to jump there.
languages
frontend
backend
databases
ml / nlp
llms / agentic ai
data viz / bi
cloud / infra
enterprise / domain
MIMS '26 — capstones, course projects, and applied research
Full-stack AI interview-prep platform (MIMS Capstone). React + Vite frontend, FastAPI backend, Neo4j graph model, Cloudflare R2 storage, Claude Haiku integration for answer drafting, verbal-practice feedback, and resume coaching.
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ANLP final project. NLP pipeline analyzing rhetorical strategies across 2,069 UN Security Council transcripts using LDA, BERTopic, and transformer-based LLMs.
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Front-end Web Architecture class project. React app fetching random jokes from an external API in a responsive interface.
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Architected and built end-to-end ETL pipeline ingesting 1,400+ municipal budget PDFs (2014–2026) across 261 Metropolitan, Municipal & District Assemblies — Ghana's first centralized fiscal transparency platform. Solo build on $51K budget.
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Co-founder & CTO. USSD-based EHR platform enabling clinics in low-resource settings to securely capture patient data offline and sync with national health systems when connectivity returns. Big Ideas Berkeley finalist.
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Full-stack scholarship platform with authentication, user dashboard, and automated scholarship scraping — built with Next.js, TypeScript, and Prisma.
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Built a Healthcare Information Management System for a clinic in Ghana on top of OpenEMR. Includes a data migration pipeline using Python and Pandas to move legacy Excel records into the new system.
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Public-facing website for the FEC clinic in Kumasi, Ghana. Marketing, services, and contact for the eye center I previously digitized.
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Built and evaluated four ML models on 3,000+ songs using Spotify, Billboard and YouTube APIs, achieving over 80% accuracy predicting chart success.
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Constructed time series models to demonstrate the impact of US Unemployment Rate announcements on QQQ, IWM, and SPY ETF prices.
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