Building intelligent systems at the intersection of machine learning optimisation, sustainable AI, and scientific computing. PhD researcher at Leeds Beckett University working on transformer efficiency, Physics-Informed Neural Networks, and Green AI for real-world impact.
I'm Moses Nnanna Ibe — a Data Analytics and AI researcher currently pursuing a PhD in Machine Learning at Leeds Beckett University, UK. My work sits at the frontier of computational intelligence and scientific discovery.
My research focuses on machine learning optimisation, transformer efficiency, Physics-Informed Neural Networks (PINNs), and Graph Neural Networks (GNNs) — with a strong commitment to sustainable and green AI systems.
I'm particularly passionate about advancing the computational efficiency and scalability of advanced AI for healthcare, environmental data science, and broader societal impact. Open to research collaborations, consulting, and academic opportunities.
Advancing transformer efficiency and scalable ML architectures for real-world deployment without compromising performance.
Physics-Informed Neural Networks and Graph Neural Networks bridging AI and scientific computing for high-fidelity modelling.
Designing AI systems that are computationally responsible — reducing energy footprint while maximising societal benefit.
Published research uncovering seasonal and temporal patterns of urban air pollutants (PM2.5, NO₂, O₃) and their differential influence on the Air Quality Index. Comparative empirical study across multiple urban environments.
Doctoral research advancing the computational efficiency of transformer architectures and exploring Physics-Informed Neural Networks for scientific simulation. Targeting scalable, energy-efficient AI systems.
Investigating GNN architectures for structured relational data — with applications in healthcare networks, molecular biology, and environmental monitoring systems.
Research into methodologies for reducing the carbon and energy footprint of large-scale ML models — exploring quantisation, pruning, and efficient inference strategies without sacrificing accuracy.
This study investigates how seasonal and temporal variations in urban air pollutants — including particulate matter, nitrogen dioxide, and ground-level ozone — differentially shape the composite Air Quality Index across multiple urban environments. Through rigorous comparative empirical analysis, the research identifies key temporal drivers of air quality degradation with implications for environmental policy and public health intervention.
More publications in progress — View full profile on ResearchGate →
Open to research collaborations, academic partnerships, AI consulting, and speaking opportunities. If you're working on problems at the intersection of machine learning, scientific computing, or sustainable AI — I'd love to hear from you.