Dr Beth Jelfs MEng, PhD

• Ph.D in Signal Processing from Imperial College London, 2010
• MEng (1^st Class Hons) in Electronic & Software Engineering from University of Leicester, 2005

Beth Jelfs is an Assistant Professor in Signal Processing and Data Analysis in the School of Engineering.

She obtained her PhD from Imperial College London and held research positions at University of Oxford (Depts. of Chemistry and Physics), University College London (Dept. Medical Physics & Bioengineering)  before joining the International Transition Team at City University of Hong Kong  (Dept. Electronic Engineering)  where she had a joint appointment as a research fellow and providing English language support to both staff and students. Following her time in Hong Kong she received a Vice Chancellor’s Research Fellowship and Lectureship at RMIT University in Australia.

Her research interests are in adaptive signal processing, in particular, she is interested in the intersection of signal processing and machine learning and how signal processing techniques can be used to inform machine learning algorithms. Her work is highly multidisciplinary ranging from the development of prototype devices for real-time monitoring of muscle fatigue to path prediction and attitude estimation for high-altitude platforms, and the creation of a toolbox for analysis of the relationships between single neuron spiking events and local field potentials for use by biologists and neuroscientists. 

She is also currently chair of the Asia-Pacific Signal and Information Processing Association’s Biomedical Signal Processing and Systems technical committee and a member of the IEEE Women in Signal Processing Subcommittee.

Beth is taking applications from students who are interested in pursuing projects in signal and image processing.

Beth's research interests are in adaptive signal processing especially statistical signal processing and signal characterisation. In particular, she is interested in the intersection of signal processing and machine learning and how signal processing techniques can be used to inform machine learning algorithms. 

Beth also has a specific interest in how these techniques can be applied to multichannel and multimodal data, particularly with reference to complex systems, for example neural data.

For further details about Beth's research please see her personal website