Data Scientist Building AI Solutions for Healthcare, Agriculture & Emerging Markets
Kano State, Nigeria., 700224, Kano, NG.
About
I am a Data Scientist and AI/ML Engineer with experience building applied AI solutions across healthcare, agriculture, and civic technology. My work focuses on machine learning pipelines, medical imaging, predictive analytics, and operational problem-solving. I have contributed to international AI collaborations through Omdena and co-authored Springer-published medical imaging research focused on brain tumor segmentation using deep learning. I enjoy building practical systems that combine research, analytics, and real-world impact.
Projects
Project: AI-assist for MRI safety and image quality: A study to develop AI tools to enhance patient safety, workflow compliance, and captured image quality for MRI in resource-limited settings.
Publication
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Summary
Executive Summary:
Magnetic Resonance Imaging (MRI) is indispensable in modern diagnostics, and expanding the effective use of MRI in Africa will yield both improvements to individual care and the means to create Africa-centric MRI datasets necessary to develop AI-assisted diagnostics suitable for African populations. But patient-safety incidents and poor image quality remain persistent challenges in much of Africa. Many MRI centres operate under heavy workloads, with limited staff training, outdated systems, and paper-based screening forms, leading to incomplete safety checks, inconsistent image quality, and a lack of structured patient metadata. These gaps hinder both patient care and the creation of usable Africa-centric MRI datasets.
Therefore, this project proposes an AI-assisted decision-support system at the pre-acquisition stage, to enhance MRI safety compliance, image quality, and complete metadata collection in resource-limited settings through three integrated modules:
Automated Pre-MRI Safety Screening, which standardizes safety protocols and assists the MR radiographers/technologists in verifying implant safety and contraindications.
Image Quality Optimization, a recommendation engine suggesting patient-specific acquisition parameters to minimize artefacts and repeat scans; and
Digital Data Recording, which replaces paper forms with a structured electronic system for reliable documentation and metadata capture. A pilot study currently underway in Nigerian imaging centres, assessing MRI safety knowledge and compliance, has revealed wide variations in practice and reliance on paper-based methods, underscoring the need for standardized, data-driven safety procedures.
Building on this preliminary evidence, this project aims to develop and test AI tools to improve safety-screening compliance, reduce repeat scans, enhance captured image quality, and ensure complete patient metadata collection. The project is clinician-led, and includes both clinical and AI teams, to ensure that the AI development is informed by and serves clinical needs. The project will establish the first structured MRI safety database in Nigeria, and will develop AI tools to advance the ability of hospitals and clinics in low-resource countries to generate MRI datasets of high quality for future use in AI research.
BRAIN-CATS: Brain Tumour Reliability-Aware Imaging with Neural Networks Using Calibration-Aware Training and Segmentation
Publication
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Summary
Abstract
Accurate and reliable brain tumour segmentation from MRI remains a clinical challenge, particularly in low-resource settings such as Sub-Saharan Africa (SSA). We present BRAIN-CATS, a segmentation framework that combines the Attention U-Net architecture with calibration-aware training to improve both accuracy and model reliability. Our approach is specifically optimized for low-resolution, multi-modal MRI data typical in under-resourced environments.
We trained the model using 5-fold cross-validation on n = 60 patients from the BraTS-Africa 2023 dataset, employing advanced preprocessing techniques, data augmentation, and a composite loss function that includes Dice, Binary Cross-Entropy, Focal Loss, and the marginal L1 Average Calibration Error (mL1-ACE). The calibration-aware component penalizes miscalibrated predictions, improving confidence estimates across tumour boundaries.
The model achieved an average Dice score of 90.38% for edema (ED), 81.94% for enhancing tumour (ET) and 78.88% for the tumour core (TC) across the folds. On external validation using 35 held-out cases, mean Dice scores were 65.30% (ET), 66.50% (TC) and 50.80% for whole tumour (WT), confirming the model’s ability to generalize to unseen data despite limited training resources. Early stopping between epochs 36–39 prevented overfitting, and stochastic weight averaging further improved generalization.
Ensemble inference using all fold checkpoints yielded smooth, consistent predictions on validation data. Visual and quantitative evaluations confirm BRAIN-CATS as a practical and resource-efficient solution for glioma segmentation in challenging imaging contexts.
Smart Farming using AI for Sustainable Agriculture in Kano State, Nigeria
Omdena Local Chapter Challenge
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Summary
Challenge Background
Agriculture remains the backbone of Kano State's economy, employing a significant portion of the population and contributing to food security in Nigeria. Despite its importance, the agricultural sector in Kano faces numerous challenges, including:
Resource inefficiency: Limited access to precise data leads to the overuse or underuse of resources such as water, fertilizers, and pesticides.
Climate variability: Unpredictable weather patterns disrupt farming cycles and yield projections.
Low productivity: Smallholder farmers lack access to advanced farming techniques, reducing overall output.
Post-harvest losses: Inefficient storage and logistics systems lead to significant waste.
Analyzing and Predicting Food Prices in Nigeria Using Machine Learning and Python
Omdena Local Chapter Challenge
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Summary
Challenge Background.
Food prices play a crucial role in the lives of Nigerians, directly impacting affordability, food security, and economic stability. This project aims to utilize Machine Learning (ML) techniques and Python programming to analyze historical food price data in Nigeria, predict future price trends, and provide valuable insights for consumers, policymakers, and stakeholders.
Work
Miccai Society
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Member
Rochester, Minnesota, US
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Summary
Active member of the MICCAI Society, contributing to the medical image computing and computer‑assisted intervention research community.
Spark Academy
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Team Coordinator
Quebec, Quebec, Canada
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Summary
Coordinating teams for educational initiatives at Spark Academy.
MyVoteMyVoice (Remote)
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Data Science / ML Engineer
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Summary
Remote data science and machine learning engineering.
Edge Line Tech Academy
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Data Analyst Facilitator
, Federal Capital Territory, Nigeria
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Summary
Facilitating data analysis learning modules and digital marketing/devops training.
Budetech Ltd (Remote)
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AI/ML Engineer Intern
Bude, England, United Kingdom of Great Britain and Northern Ireland
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Summary
Internship focused on AI/ML engineering and consulting.
Costus Rice
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Data Analyst
Kano, Kano, Nigeria
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Summary
Analyzed business data to inform product development, process optimization, and market strategies.
Omdena
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Data Scientist / ML Engineer / Chapter Lead
New York, New York, US
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Summary
Led a chapter and executed data science / ML engineering projects remotely.
Intern Research Data Analyst (Quantm Analytics NG?)
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Intern Research Data Analyst
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Summary
Internship conducting financial research data analysis.
The Sparks Foundation
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Data Science & Business Analytics
Singapore, Central Region, Singapore
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Summary
Developed analyses in data science and business analytics within an education management context.
Gezawa Commodity Market & Exchange Ltd
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Sales Marketing Analyst
Gezawa, Kano, Nigeria
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Summary
Conducted market research, sales analytics, and coordinated marketing efforts in commodities.
Quantumanalytics NG
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Data Analyst Fellow
London, England, United Kingdom of Great Britain and Northern Ireland
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Summary
Data analyst fellowship focused on analytics for technology and internet solutions.
