Dr Kenneth Omokhagbo Afebu

Kenneth currently holds a postdoctoral research fellow position in Engineering department where he completed his PhD with Prof. Yang Liu and Dr Evangelos Papatheou as his supervisors. His research focuses on investigating and analysing the nonlinear phenomena of dynamic systems for the purpose of developing machine learning (ML) models capable of characterising and predicting the long-term behaviours of the systems. This is useful for ensuring the reliability, stability, longevity, optimal performance and energy efficiency of such systems, especially when they have the tendency to encounter multi-stability during operation. His work also involves using these intelligent ML models to predict the varieties of unknown conditions that the system may encounter during operation. The later initiated his work on the use of ML and drill-bit vibrations for monitoring bit -rock impact responses in a rotary-percussive drilling system and also for mapping inhomogeneities in downhole rock layers during deep hole drilling.  These models thus guide the driller in adjusting the drilling load parameters to maintain desirable impact responses while also avoiding excessive loading that may compromise the borehole integrity especially at transition zones.

As a novel initiative, he has also extended this approach to harness the sensitivity of the nonlinear phenomena exhibited by small-scale robots that are in direct contact and interacting with human tissue for disease diagnosis using the power of AI. This is based on the fact that, similar to the inhomogeneities observed in downhole rock layers, different disease conditions present inhomogeneities in their affected tissues. These inhomogeneities are easily picked up and reflected in the exhibited dynamics of a small-scale robot that is in contact and interacting with the tissues, thus presenting a non-visual method of soft-tissue examination.

Research highlights:

As a member of the Exeter Small-Scale Robotics Laboratory, Kenneth’s research highlights include:

• Robotic capsules encountering lesions: Explored the use of AI and the resulting dynamics of a self-propelled robotic capsule travelling and  encountering lesions in the bowel for the purpose of detecting malignant transformations in the lesions.

• Micro-particles in blood channels: Exploring the power AI and the nonlinear behaviour of micro-particles in blood channels that are in close proximity to cancer sites to identify metastasis and other intravascular diseases.

• Explainable AI and federated learning in medicine: Explainable AI coupled with federated learning create a framework that supports collaboration and innovation, enabling health institutions to collectively train more generalisable and reliable AI models without compromising individual data security.

Collaborations:

In carrying out my research, Kenneth collaborates closely with Professors and Clinicians the fields of:

• Dynamics and control,

• Applied mathematics and

• Gastroenterology

Impact and vision:

Kenneth’s research aim is to revolutionise the modalities of carrying out medical examination during disease diagnosis by taking advantage of the nonlinear sensitivity of micro-robots and their ability to access hard-to-reach anatomy. He will rely on the ability of AI to learning complex nonlinear relationships from data, to map the resulting dynamics of the micro-robots to different disease conditions. This way patients can be offered minimally invasive, but yet, highly effective diagnostic procedures which in the long term may become self-administrable over time. Thus, allowing more people to be tested and reducing the need for initial face-to-face appointments with clinicians.

Before now, Kenneth has worked on the development of a smart system for detecting and quantifying gas leaks along a gas transmission line using machine learning and measurable pipe flow dynamics. His work and research experience also traverses the use of geophysical methods for subsurface investigation, rock core drilling for mineral exploration and groundwater assessment and development.

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 coming to the UK for his Master of Science degree in Petroleum and Gas Engineering with distinction at the University of Salford.