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 newly developed self-propelled robotic capsule for early cancer detection using machine learning.
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 impact modes categorisation and downhole rock 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 of Dr Yang Liu’s self-propelled endoscopic capsule for detecting hard-to-visualise early bowel cancers. The idea is based on the fact that, like downhole rock units, cancerous lesions right from onset, present biomechanical inhomogeneities that are reflected in the dynamics and long-term behaviours of their impacting system such as a robotic capsule traversing the bowel.
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 and research experience have revolved around geological and geophysical investigations for structural integrity and mineral exploration, core drilling for mineral deposit assessment, flow dynamics for pipeline monitoring, vibration signal analysis for system characterisation and machine learning with real-life application.