Dr Kenneth Afebu
Postdoctoral Research Fellow
Telephone: 01392 724536
Extension: (Streatham) 4536
Kenneth is a Postdoctoral Research Fellow in the College of Engineering, Mathematics & Physical Sciences, and a member of the Applied Dynamics and Control Laboratory. He is currently involved in an EPSRC New Horizons project exploring intelligent models for early and hard-to-visualise bowel cancer detection. With bowel cancer being the second most common cancer in Europe and the second deadliest in the UK, Kenneth will be exploring the dynamics of a new self-propelled endoscopy capsule for early cancer detection using machine learning methods.
Kenneth completed his PhD studies at the University of Exeter under the Applied Dynamic and Control Laboratory where he investigated the rich dynamics of a rotary-percussive drilling system and machine learning methods for downhole rocks and drilling modes characterisation. He was supervised by Dr Yang Liu and Dr Evangelos Papatheou under the sponsorship of the Petroleum Technology Development Fund (PTDF) of the Federal Republic of Nigeria. He plans to extend the knowledge from his PhD study in exploring the impact dynamics and multi-stability characteristics of Dr Yang Liu’s self-propelled endoscopy capsule for detecting hard-to-visualise early bowel cancers. The idea is based on the fact that, similar to downhole rock layers, cancerous bowel tissues present inhomogeneities which are reflected in the dynamics and long-term behaviours of the traversing robotic capsule being a nonlinear dynamical system, and can be analysed to differentiate between healthy and potential cancerous tissues as early as possible.
Kenneth holds a BSc (Hons) degree in Geology from the University of Ibadan, Nigeria, and a Master of Science degree in Applied Geophysics from the same university before proceeding to the University of Salford, UK where he obtained a Master of Science degree in Petroleum and Gas Engineering with distinction. Over the years, Kenneth’s work experience and research have revolved around geological and geophysical investigations for structural integrity and mineral exploration, core drilling for mineral deposit assessment, pipeline dynamics for flow monitoring, vibration signal analysis for system characterisation and machine learning with real-life application.