Summary
Lukman Ismaila is a research scientist and machine learning engineer with a decade of experience bridging advanced neuroimaging research and production AI systems. At Johns Hopkins School of Medicine he builds scalable fMRI pipelines and develops interpretable, AI-driven biomarkers using dynamic connectivity, graph theory, and multilayer network models to study brain plasticity and disease. Parallel work leading ML teams at Omdena shows he can take models to production—designing multi-agent conversational systems, RAG pipelines, real-time APIs, and voice-enabled interfaces for clinical and social impact. His background spans a PhD in deep learning for medical imaging, academic teaching, and hands-on product development, enabling him to translate complex computational methods into clinically actionable tools. Colleagues value his interdisciplinary collaboration style and the uncommon combination of rigorous neuroimaging methods with practical deployment experience.
9 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD Deep Learning Application in Medical Imaging, Doctor of Philosophy - PhD Deep Learning Application in Medical Imaging at Université d'Angers
MSc Computer science Medical Informatics, MSc Computer science Medical Informatics at Nile University of Nigeria
MSc Computer Science Adaptive Learning Framework, MSc Computer Science Adaptive Learning Framework at African University of Science and Technology (AUST)
ebira, English, Hausa, Turkish, French