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Faculty of Science, Engineering and Computing.
Penrhyn Road
Kingston upon Thames
Surrey KT1 2EE

Tel: +44 (0)20 8417 9000

Dr Jean-Christophe Nebel

School/Department: Computer Science & Mathematics
Position: Associate Professor


Dr Jean-Christophe Nebel is an Associate Professor in Computing Science and Bioinformatics in the Faculty of Science, Engineering and Computing at Kingston University. He holds an MScEng from the Institute of Chemistry and Industrial Physics in Lyon (French Grande Ecole), an MSc and a PhD in Computing Science from the University of St-Etienne (France, 1997). He is also a Senior Fellow of the Higher Education Academy (SFHEA).
After his PhD, he joined the Department of Computing Science of the University of Glasgow, where he worked as a research assistant and as a research fellow for 7 years. He was co-investigator and line manager of a postdoctoral researcher for a 3 year EU-IST project (IST-2000-28094, 2001-04). He and co-authors developed the world first experimental 3D television studio and won the IEE Reeve Premium award in 2004 for a journal paper reporting that work.
Since he arrived at Kingston University, he has conducted research in two areas - computer vision and bioinformatics - where he has developed novel pattern recognition algorithms.
Computer vision
He is co-leader of the Human Body Motion Group (HBM) which focuses on the extraction and analysis of human motion using video footage. As the principal investigator of the EPSRC funded project MEDUSA (EP/E001025/1, 2006-09), he led the design and implementation of machine learning techniques for video data analysis, especially human action recognition. Based on that research, he secured contracted research from a multinational engineering and electronics company (2010-11). Since then, he has pioneered with international collaborators usage of common sense reasoning to enhance computer vision-based action recognition. That novel approach was featured in 'Communications of the ACM' in December 2014.
He has also been developing a new bio-inspired framework for computer vision. Early results have led to a Best paper prize award at the international EvoStar conference.
As leader of the Bioinformatics Research Group at Kingston University, his research activities span across different aspects of bioinformatics including protein function and structure prediction, protein interaction and protein active site modelling. With the support of grants from the British Council, the EU, the Polish Ministry of Science and the Royal Society, he investigated, in collaboration with the bioengineering group at Wroclaw University of Science and Technology (Poland), usage of formal grammars for 3D structure modelling of membrane proteins (2007-13).
More recently, he has led the development of novel approaches for the 3D structure prediction of both single chain and complexed proteins. His research is being applied on proteins involved in medical conditions (cataract and diabetes) through collaborations with life scientists.

Educational and Professional Qualifications

  • 2015 - present Senior Fellow of the Higher Education Academy (SFHEA)
  • 2004 - 2005 Postgraduate Certificate in Learning & Teaching in Higher Education at Kingston University, London (UK)
  • 1994 - 1997 PhD in Computing Science at the University of St-Etienne (France)
  • 1991 - 1992 MSc in Computing Science at the University of Lyon (France)
  • 1987 - 1992 MScEng in Electronics & Data Processing at CPE Lyon (France)

Research Interests

Computer Vision
- Bio-inspired Computer Vision
- Action & Pose Recognition
- Common sense reasoning for Action recognition
- Dimensionality Reduction
- Protein function prediction
- Protein interaction prediction
- Protein structure prediction
- Protein active site modelling
- Optical Function of Proteins in the Eye

Teaching Area

  • CI4200-IT-Toolbox (Module leader)
  • CI6300-Individual projects (Module co-leader)
  • CI7510-Real Time Programming
  • CI7300-Data Management and Governance
  • LS7009-Biotechnology


    +Professional Experience



+44 (0) 208 417 2740