[ WHO I AM ]
About.
I'm a computer-science graduate from Landmark University who does machine learning where the data is worst — hospital imaging archives, half-scanned government records, spreadsheets nobody trusts. My thesis fused MedCLIP image embeddings with TabNet clinical features and beat both single-modality baselines. Two internships taught me that shipped automation beats clever prototypes. Every project I publish documents what didn't work, not just what did.
[ CAREER ]
Experience.
// 2021 — present- automated the firm's weekly consulting reports end-to-end: data collection to formatted memo
- designed, built, and shipped the corporate website
- automated document-processing workflows across departments, cutting manual handling time by ~90%
- built validation scripts that caught entry errors before they reached official records
- thesis: multimodal breast-cancer detection — image + clinical fusion, beating unimodal baselines
- focus areas: machine learning, databases, distributed systems
[ TECHNICAL MATRIX ]
Capabilities & Stack.
// verified tools & frameworks
// ml-ai
pytorch
scikit-learn
medclip
tabnet
dinov2
grad-cam
// data-engineering
postgresql
selenium
pandas
etl pipelines
schema validation
// automation-web
python
fastapi
docker
html5/css3/js
SYSTEM_ARCHITECTURE
├── Multimodal ML // Vision (MedCLIP/ViT) + Tabular (TabNet)
├── Resilient ETL // Selenium scraper pool + PostgreSQL validation
└── Deployment // Docker containers + Cloudflare static edge hosting
[ QUICK FACTS ]
At a glance.
// for the 90-second reader- education
- B.Sc. Computer Science, Landmark University
- focus
- Multimodal ML · medical imaging · data infrastructure
- stack
- Python · PyTorch · PostgreSQL · FastAPI · Docker
- location
- Abuja, Nigeria — remote-friendly (WAT)
- status
- Open to ML Engineer & Data Scientist roles
- elsewhere
- GitHub ↗ · LinkedIn ↗ · CV (PDF) ↓
// certifications — deeplearning.ai deep learning specialization · google data analytics · full list on linkedin ↗