Imaging, Data and Communications

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Dr Jiabin Jia obtained his BEng (2002) in Electronics Engineering and MSc (2005) in Electrical and Electronics Engineering at Wuhan University, China. After worked one year in the Hardware R&D department of H3C Technology Co. Ltd, China, Dr Jia started his PhD study supported by the ORSAS scholarship from 2006 and achieved his PhD degree in 2010 at The University of Leeds, UK. Following three years working on an EPSRC project as a Research Fellow, he started to work at the School of Engineering, The University of Edinburgh from October 2013.   Dr Jia’s expertise is in the area of sensing physics, electronic instrumentation, process measurement and analysis. The research in Dr Jia’s laboratory takes an approach of synergistic integration of physics, modelling, optimization and design, to provide innovative sensing solutions for various challenging problems.   So far, he has been awarded research funding more than £500k as PI and £2M as Co-I. Dr Jia is maintaining close collaborations with industrial partners. A number of technological breakthroughs were developed, patented and licensed for commercialisation to the world-leading industrial company in process tomography, Industrial Tomography Systems plc (ITS). His work attracted interest from international company. After a worldwide technology tender, Fuji Electric Ltd invested £300K on commissioned research basis to develop a wet gas metering technology. In 2016 and 2018, Dr Jia undertook consultancy work for the National University of Ireland Galway and Pennsylvania State University to provide technical advice. So far, more than 40 peer-reviewed journal and two book chapters has been published. Two patents were granted. Three PhD students have successfully achieved degree under his supervision. At present, he is supervising five PhD students and two PDRAs.

• 2019 Postgraduate Certificate Academic Practice (PgCAP), Univesrity of Edinburgh, UK
• 2010 Doctor of Philosophy (PhD), School of Chemical and Process Engineering, University of Leeds, UK
• 2005 Master in Science (MSc), School of Electrical Engineering, Wuhan University, P. R. China
• 2002 Bachelor of Science (BEng), School of Electrical Engineering, Wuhan University, P. R. China

• Fellow of the Higher Education Academy (FHEA)
• Senior Member of Institute of Electrical and Electronics Engineers (SMIEEE)
• Member of consultative scientific panel of Interntional Society for Industrial & Process Tomography
• Associate editor of Measurement Journal of the International Measurement Confederation

• Software and Embedded Systems Laboratory 2 (ELEE08022)
• Digital System Design and Digital System Laboratory 3 (ELEE09035)
• Engineering Mathematics 2B tutorial (SCEE08010)
• Supervision for BEng, MEng, MSc and PhD projects.

I lead a research group focused on developing and applying signal processing algorithms to biomedical data. Our main aim is to reveal the subtle changes that major diseases (e.g., Alzheimer's and epilepsy) cause in the brain activity and how this changes in different conditions and mental states.

In October 2013, I joined the Institute for Digital Communications, School of Engineering, as a Chancellor's Fellow in biomedical signal processing. I was tenured in August 2016 and promoted to Senior Lecturer in August 2020.

Previously, I held a post-doctoral position at the School of Computing and Mathematics of Plymouth University.

My training includes an MEng in telecommunications engineering from the University of Valladolid (Spain) in 2005 and a PhD in biomedical signal processing from the same university in 2010.

• 2010 - PhD in Telecommunications Engineering - University of Valladolid (Spain)
• 2005 - MEng in Telecommunications Engineering (First) - University of Valladolid (Spain)

• Senior Member of the IEEE
• Fellow of the Higher Education Academy

• Programme Director of the MSc in Signal Processing and Communications
• Course organiser of Image Processing
• Teacher in Engineering Software 3
• Tutor in Engineering Mathematics 2A
• Supervision of final year projects and MSc theses

• Biomedical signal processing
• Brain connectivity
• Graph theory
• Machine learning
• Nonlinear analysis