Ahmed Sulemana Profile

Ahmed Sulemana is an MPhil student in Mathematical and Computational Physics at Kwame Nkrumah University of Science and Technology (KNUST), Ghana, where he also earned his BSc in Mathematics. His undergraduate thesis applied a Neural ODE, a continuous-depth neural network, to body cooling dynamics for estimating time of death, and benchmarked it against classical numerical solvers. This work was presented as a poster at Posterfest 24.

His current research focuses on turbidity prediction in water treatment, using physics-informed Neural ODE approaches to support coagulant dosing at the Barekese Water Treatment Plant. Separately, he carried out a systematic review of digital twin-oriented, hybrid physics-informed Neural ODE frameworks for modelling aeration and oxygen transfer efficiency in wastewater treatment plants, and presented it as a poster at the African International Conference on Quantum Computing and Simulation, where he also served as a member of the Scientific Committee. He has further broadened his grounding in water systems through the IWA DataQ webinar series and the CeTAmb summer school on climate-resilient WASH solutions.

Beyond his studies, Ahmed has worked as an IT technician with Ghana Technical and Vocational Education and Training and as a Budget Officer with the Asunafo North Municipal Assembly. He is proficient in Python, MATLAB and Power BI, completed the iiAfrica Industry Immersion Program, where he built a student performance analysis dashboard, and has served as Ahafo Zonal Coordinator for Ghana Grows.