Dr Beth Jelfs researches signal processing in machine learning applications which influences technology development in areas such as clinical modelling, prosthetics and rehabilitation.
signal processing; machine learning; biomedical engineering
Dr Jelfs’ research examines signal characterisation in machine learning applications. Dr Jelfs’ goal is to understand more about the nature of signals and the mechanisms through which they are generated, in order to inform the choice of machine learning frameworks. Dr Jelfs applies these research interests to biomedical data and developing adaptive statistical signal processing techniques that can identify and track changes in biological signals.
Through a greater understanding of signal-generation mechanisms, Dr Jelfs aims to provide information which is more biologically and clinically relevant. This is particularly important when developing new technologies in areas such as clinical modelling, prosthetics and rehabilitation.
Dr Jelfs earned her PhD in signal processing from Imperial College London in 2010. She subsequently held postdoctoral positions in next-generation DNA sequencing at the University of Oxford and biomedical optics at University College London.
Dr Jelfs joined the International Transition Team at City University of Hong Kong to improve the level of English proficiency at the university. She was on the organising committee for ‘enGENEious’, a student and post-doctoral organised conference on microbial engineering and was an event manager for the inaugural ‘Pint of Science’ festival, which has since become an annual global event.
Dr Jelfs was awarded a Vice-Chancellor’s Research Fellowship in 2017 and is based in the School of Engineering.