Dr Edward Sanderson

Edward has published research on the use of machine learning and data-driven methods. He recently completed his PhD, where he proposed new approaches for the generation of residential electricity consumption profiles. This aimed to more accurately represent demand in simulation-based studies of energy systems undertaken to inform and coordinate low-carbon developments. Edward has since taken up a post-doctoral research position on the STFC-funded AIdDeCo project, with a focus on developing methods for computer vision tasks in colonoscopy, to help improve the detectability and treatment of colorectal cancer. He also teaches the Artificial Intelligence and Machine Learning module.

Edward completed his MEng in Computer Aided Engineering, where he became fascinated with computational methods for simulating complex systems and took a particular interest in the simulation of energy systems. He then worked towards his PhD, which focused on developing methods for simulating residential electricity consumption profiles. He investigated the use of deep learning for this purpose and proposed novel generative adversarial networks for simulating appliance load, while also addressing the shortage of suitable evaluation metrics.

Following the completion of his PhD, Edward has worked on the STFC-funded AIdDeCo project aimed at developing methods for computer vision tasks in colonoscopy and has proposed novel state-of-the-art neural network architectures for polyp segmentation and monocular depth estimation. He has also developed computational tools required for the MILC-DITI project investigating the use of digital thermal imaging to assess the breast-feeding Mother-Infant dyad Lactation process.

Edward began his teaching experience while pursuing his PhD where he supported the teaching of simulation, mathematics, artificial intelligence, machine learning, and computer vision. He now focuses on the delivery of the Artificial Intelligence and Machine Learning module.

Throughout his academic experience, Edward has published a number of research papers and given talks on his research at international conferences. He has also competed in and won challenges involving the development of new methods for tasks relevant to his research interests. Edward also reviews papers on the use of machine learning and data-driven methods for biomedical and health applications.