I architect end-to-end ML pipelines — from k-space data and model design through to deployment — across medical imaging, robotics, NLP, and agentic AI. First Runner-Up at the RAIN × Meta AI Developer Academy Nigeria Hackathon, and a UN Millennium Fellow.
I'm an AI/ML Engineer and Mechatronics Engineering student at the Federal University of Technology Minna, focused on building intelligent systems that leave the notebook and reach real users. My core research is physics-informed deep learning for low-field MRI reconstruction — extending the End-to-End Variational Network with physics priors to make faster, more affordable scans viable in resource-limited settings.
I'm currently applying that same end-to-end discipline as an AI Robotics Engineer Intern at Coderina Edtech and a Machine Learning Intern at FlyRank AI, working across robotics, computer vision, and production ML pipelines. Earlier, I led a 13-person team building a road-hazard detection system, shipped a multilingual AI loan advisor for rural farmers, and scaled an agentic productivity bot to 65+ active users — each time owning the pipeline from data to deployment.
A quick path through where I've studied, worked, and been recognized.
Building an industrial-sorting robotic arm and an autonomous robotic dog; teaching AI fluency and LEGO Spike to young learners.
Building end-to-end machine learning and deep learning pipelines.
Delivered a production AI booking automation system that cut processing time by 60% and onboarded 20+ riders; provided bilingual (English/Hausa) support.
Nationally recognized for applied AI innovation and engineering excellence.
Selected globally for leadership, innovation, and social impact contributions.
Grew community engagement by 40% and resolved 95%+ of customer inquiries within SLA in a high-volume environment.
Low-field MRI scanners produce noisy, low-resolution images with long scan times — a real barrier in resource-limited settings like Nigeria. I extended the End-to-End Variational Network with physics-informed priors from the MRI forward model, built a full k-space preprocessing pipeline with retrospective undersampling, and validated reconstruction quality across multiple acceleration factors using SSIM, PSNR, and NMSE.
Led a 13-member team to detect road cracks, potholes, and bumps from images for smart infrastructure use cases, evaluated on precision, recall, and F1.
An autonomous indoor robot that navigates to workstations on command, with a real-time web command center for telemetry and control.
Financial literacy AI for rural farmers, processing loan documents in 8 Nigerian languages via OCR, ASR, TTS, and an LLM risk-assessment engine.
An autonomous Telegram productivity bot scaled to 65+ active student users through progress logging and peer-competition features.
A Telegram-based booking system for Togo Mobility, automating registration and payment verification — cutting manual booking time by 60%.
Open to AI/ML roles, research collaboration, and interesting problems. Reach out directly, or use the form.
inuwamuhammad930@gmail.com +234 916 873 0302 linkedin.com/in/muhammad-inuwa-muhammad github.com/shadowboy-tech